Imaging system with dynamic image sensor configuration adjustment

Abstract

This document discloses an imaging system (and associated systems, apparatus, and methods) with dynamic image sensor configuration adjustment. In one embodiment, an imaging system includes a pixel array, an event detection circuitry, an ambient light monitor, and a sensor controller operatively coupled to each of the event detection circuitry and the ambient light monitor. The sensor controller may be configured to: (i) receive one or more event signals from the event detection circuitry and an ambient light signal from the ambient light monitor; (ii) determine, at least in part, a contrast threshold for the event detection circuitry or an optimized value set by another sensor based on the one or more event signals and the ambient light signal, the optimized value being usable by the imaging system to improve the image quality of one or more images of an external scene; and (iii) transmit the determined optimized value to the event detection circuitry.

Description

[0001] Cross-referencing related applications

[0002] This application claims priority to U.S. Provisional Application No. 63 / 774,674, filed on March 19, 2025, which is hereby incorporated in its entirety by reference. Technical Field

[0003] This disclosure generally relates to image sensors, and specifically, but not exclusively, to hybrid image sensors. For example, several embodiments of the present technology described in detail below pertain to on-chip implementations for scene content detection, such as other configurations for adjusting a nominal contrast threshold or an event visual sensor. Background Technology

[0004] Image sensors have become ubiquitous and are now widely used in digital cameras, cellular phones, security cameras, and in medical, automotive, and other applications. As image sensors are integrated into a wider range of electronic devices, it is expected that their functionality and performance metrics can be enhanced in as many ways as possible (e.g., resolution, power consumption, dynamic range) through both device architecture design and image acquisition and processing. The technologies used to manufacture image sensors continue to advance rapidly. For example, the demand for higher resolution and lower power consumption has driven further miniaturization and integration of these devices.

[0005] A typical image sensor operates in response to image light incident on it from an external scene. The image sensor includes a pixel array with photosensitive elements (e.g., photodiodes) that absorb a portion of the incident image light and immediately generate an image charge upon absorption. The image charge generated by the pixel light can be measured as an analog output image signal on a bit line, which varies as a function of the incident image light. In other words, the amount of image charge generated is proportional to the intensity of the image light, which is read out as an analog image signal from the bit line and converted into a digital value to produce a digital image (e.g., image data) representing the external scene. The analog image signal on the bit line is coupled to a readout circuit that includes an input stage with an analog-to-digital converter (ADC) to convert those analog image signals from the pixel array into digital image signals. Summary of the Invention

[0006] In one aspect, this disclosure provides an imaging system comprising: a pixel array including a plurality of pixels arranged in a plurality of rows and columns, each of the plurality of pixels including a photoelectric sensor configured to generate a photocurrent in response to incident light received from an external scene; an event detection circuit system operatively coupled to the pixel array, wherein the event detection circuit system is configured to receive the photocurrent from one or more of the photoelectric sensors and output one or more event signals based at least in part on the received photocurrent and a contrast threshold, wherein each of the one or more event signals indicates an event detected in the external scene; and an ambient light monitor operatively coupled to the pixel array, wherein... An ambient light monitor is configured to output an ambient light signal indicating the ambient light level of the external scene; and a sensor controller is operatively coupled to each of the event detection circuitry and the ambient light monitor, wherein the sensor controller is configured to: receive one or more event signals from the event detection circuitry and the ambient light signal from the ambient light monitor; determine, at least in part, based on the one or more event signals and the ambient light signal, an optimized value for the contrast threshold set for the event detection circuitry or another sensor, the optimized value being usable by the imaging system to improve the image quality of one or more images of the external scene; and transmit the optimized value to the event detection circuitry.

[0007] In another aspect, this disclosure provides a sensor processor for an imaging system, the sensor processor comprising: a sensor controller operatively coupled to an event detection circuitry of the imaging system and an ambient light monitor of the imaging system, wherein the sensor controller, when coupled to the event detection circuitry and the ambient light monitor, is configured to: receive one or more event signals from the event detection circuitry and an ambient light signal from the ambient light monitor; determine, at least in part, a contrast threshold for the event detection circuitry or an optimized value set by another sensor based on the one or more event signals and the ambient light signal, the optimized value being usable by the imaging system to improve the image quality of one or more images of an external scene; and transmit the optimized value to the event detection circuitry.

[0008] In another aspect, this disclosure provides a method for operating an imaging system, the method comprising: generating a photocurrent using a photoelectric sensor comprising a plurality of pixels in a pixel array in response to incident light received from an external scene; outputting one or more event signals based at least in part on the photocurrent and a contrast threshold, wherein each of the one or more event signals indicates an event detected in the external scene; generating an ambient light signal indicating an ambient light level of the external scene; determining, at least in part on the one or more event signals and the ambient light signal, an optimized value for the contrast threshold or another sensor setting for the imaging system, the optimized value being usable by the imaging system to improve the image quality of one or more images of the external scene; and transmitting the optimized value to a component of the imaging system for adjusting the contrast threshold or the other sensor setting. Attached Figure Description

[0009] The following description, with reference to the figures, outlines non-limiting and non-exhaustive embodiments of the present technology, wherein similar or analogous reference numerals are used throughout to refer to similar or analogous components unless otherwise specified.

[0010] Figure 1 The illustration depicts a stacked hybrid complementary metal-oxide-semiconductor (CMOS) image sensor (CIS) and event vision sensor (EVS) imaging system configured according to an embodiment of the present technology.

[0011] Figure 2 This is a schematic block diagram of a model training scheme configured according to an embodiment of the present technology.

[0012] Figure 3 This is a schematic block diagram of a hybrid imaging system configured according to an embodiment of the present technology.

[0013] Figure 4 The diagram illustrates an event-driven circuit configured according to an embodiment of the present technology.

[0014] Figure 5 This is a schematic block diagram of the event detection section of a hybrid imaging system configured according to an embodiment of the present technology.

[0015] Figure 6 This is a table listing various scenario content parameters according to embodiments of the present technology.

[0016] Figure 7 This is a flowchart illustrating a method of operating an imaging system according to an embodiment of the present technology.

[0017] Those skilled in the art will understand that the elements in the figures are illustrated for simplicity and clarity and are not necessarily drawn to scale. For example, the dimensions of some elements in the figures may be enlarged relative to other elements to aid in understanding various aspects of the art. Furthermore, common but well-known elements or methods that are useful or necessary in commercially feasible embodiments are generally not depicted in the figures or described in detail below to avoid unnecessarily obscuring the description of various aspects of the art. Detailed Implementation

[0018] This disclosure relates to imaging systems (and associated systems, apparatus, and methods) capable of dynamically (e.g., automatically and / or in real-time) optimizing hardware operating parameters. For example, several embodiments of this technology pertain to image sensors that can operate in a mixed mode to simultaneously capture CIS and EVS information and determine optimal sensor settings (e.g., an optimal nominal contrast threshold) to improve or maximize image quality. Such image sensors may include a sensor controller that receives event signals and ambient light signals, determines optimal values ​​for one or more sensor settings, and transmits these optimal values ​​to other components of the image sensor, such as event-driven circuitry.

[0019] In the following description, specific details are set forth to provide a thorough understanding of various aspects of the art. However, those skilled in the art will recognize that the systems, apparatuses, and techniques described herein can be practiced without the one or more of the specific details set forth herein or using other methods, components, materials, etc.

[0020] The references to “example” or “implementation” in this specification mean that a particular feature, structure, or characteristic described in connection with the example or embodiment is included in at least one example or embodiment of the present technology. Therefore, the phrases “for example,” “as an example,” or “implementation” as used herein do not necessarily all refer to the same example or embodiment, and are not necessarily limited to the specific example or embodiment discussed. Furthermore, the features, structures, or characteristics of the present technology described herein can be combined in any suitable manner to provide further examples or embodiments of the present technology.

[0021] For ease of description, spatial relative terms (e.g., “below,” “under,” “above,” “below,” “over,” “upper,” “top,” “top,” “left,” “right,” “center,” “middle,” etc.) are used herein to describe the relationship of an element or feature relative to one or more other elements or features illustrated in the figures. It will be understood that, in addition to the orientations depicted in the figures, spatial relative terms are intended to encompass different orientations of the device or system during use or operation. For example, if the device or system illustrated in the figures is rotated, turned, or flipped about a horizontal axis, then an element or feature described as “below,” “under,” or “below” one or more other elements or features may be oriented “above” one or more other elements or features. Therefore, the exemplary terms “below” and “below” are non-limiting and can encompass both above and below orientations. Alternatively or additionally, the device or system may be oriented in other ways than illustrated in the figures (e.g., rotated 90 degrees about a vertical axis, or otherwise), and the spatial relative descriptive terms used herein shall be interpreted accordingly. Additionally, it will be understood that when an element is referred to as being “between” two other elements, the element may be the only element between the two other elements, or there may be one or more intervening elements.

[0022] It will be understood that while the terms first, second, third, etc., may be used in this disclosure and claims to describe various elements, these elements should not be limited by these terms and should not be used to determine the process sequence or formation order of related elements. Unless otherwise indicated, these terms are used only to distinguish one element from another. Therefore, the first element discussed below may be referred to as the second element without departing from the teachings of the disclosed embodiments.

[0023] It should be understood that the terms "photoelectric sensor" or "photodiode" can correspond to a doped region disposed within a semiconductor material, configured to generate an image charge (e.g., one or more electrons or holes) in response to incident light. For example, a photodiode can correspond to an n-type doped region disposed within a p-type semiconductor material, or an n-type doped region surrounded by a p-type well disposed within a semiconductor material, or a p-type doped region disposed within an n-type semiconductor.

[0024] Several technical terms are used throughout this specification. These terms will be given their general meaning in the field of their respective domains, unless otherwise specifically defined herein or the context in which they are used will clearly imply otherwise. It should be noted that in this document, component names and symbols are used interchangeably (e.g., Si and silicon); however, they have the same meaning.

[0025] A. Overview

[0026] Many image sensors, such as event vision sensors and hybrid CIS / EVS image sensors, employ event vision pixels that can be used to detect events in an external scene. For example, an event vision pixel can detect an event when the relative logarithmic brightness of the light incident on it from the external scene exceeds a predefined threshold (called the contrast threshold). When using a larger contrast threshold, a larger relative logarithmic brightness is required before the event vision pixel detects the event. Therefore, using an excessively large contrast threshold can lead to an increase in the chances that the event vision pixel "misses" real events occurring in the external scene. On the other hand, when using a smaller contrast threshold, a smaller relative logarithmic brightness can trigger the event vision pixel to detect the event. Therefore, using an excessively small contrast threshold can lead to the event vision pixel detecting an increased number of noise events, which are events caused by noise recorded by the event vision pixel rather than real events in the external scene. Thus, the quality of the event data (and therefore the quality of the image based on the event data) can be directly related to the contrast threshold. However, the optimal contrast threshold can be scene- and / or application-specific and can depend on a variety of other factors, such as coupling capacitors or bias current in the event vision pixel.

[0027] Therefore, this technology is directed to imaging systems (and associated systems, apparatus, and methods) that can, for example, automatically and / or in real time optimize their own hardware operating parameters (e.g., nominal contrast threshold). For example, several embodiments of this technology are directed to various imaging systems having a sensor controller configured to: (a) receive one or more inputs (e.g., event signals, ambient light signals, noise signals) between CIS image frames, and (b) determine, at least in part, optimized values ​​set by one or more sensors (e.g., nominal contrast threshold of event visual pixels) based on one or more inputs.

[0028] Specifically, in one embodiment, the imaging system includes a sensor controller that uses measured or acquired data (e.g., event data) to optimize various hardware settings in real time. For example, the hardware settings to be optimized may include a nominal contrast threshold, coupling capacitance, current applied by a current source, and / or other settings of the event-driven circuitry. In some embodiments, a learning algorithm is employed to help determine optimal values. In these and other embodiments, feedback loops are integrated into the system to continuously optimize hardware settings. In these and other embodiments, measurements are used to first identify the type and / or condition of the captured external scene, and then a lookup table is referenced to determine optimal values ​​at least in part based on the identified type and / or condition of the captured external scene.

[0029] Therefore, as will be shown and described in the various examples below, an imaging system configured according to some embodiments of the present technology may include a pixel array, an event detection circuitry operatively coupled to the pixel array, an ambient light monitor operatively coupled to the pixel array, and a sensor controller operatively coupled to each of the event detection circuitry and the ambient light monitor. The pixel array may include a plurality of pixels arranged in a plurality of rows and columns, individual pixels of which include a photoelectric sensor configured to generate a photocurrent in response to incident light received from an external scene. The event detection circuitry may be configured to receive the photocurrent from the photoelectric sensor and output one or more event signals based at least in part on the received photocurrent and a contrast threshold. Each of the one or more event signals may indicate an event detected in the external scene. The ambient light monitor may be configured to output an ambient light signal indicating the ambient light level of the external scene. The sensor controller may be configured to: (i) receive one or more event signals from the event detection circuitry and an ambient light signal from an ambient light monitor; (ii) determine, at least in part, an optimal value for at least one of the event detection circuitry's contrast threshold or another sensor setting (e.g., which is expected to improve or maximize the image quality of the external scene) based on the one or more event signals and the received ambient light signal; and (iii) transmit the determined optimized value to the event detection circuitry in real time.

[0030] A sensor processor included in a hybrid imaging system and configured according to some embodiments of the present technology may include a sensor controller operatively coupled to an event detection circuitry of the hybrid imaging system and an ambient light monitor of the hybrid imaging system. The sensor controller may be configured to: (i) receive one or more event signals from the event detection circuitry and an ambient light signal from the ambient light monitor; (ii) determine, at least in part, an optimal value for at least one of the event detection circuitry's contrast threshold or another sensor setting (e.g., one that is expected to improve or maximize image quality of the external scene) based on the received event signals and the received ambient light signal; and (iii) transmit the determined optimal value to the event detection circuitry in real time.

[0031] A method for operating a hybrid imaging system according to some embodiments of the present technology may include: (i) generating a photocurrent in response to incident light received from an external scene using a photoelectric sensor comprising a plurality of pixels in a pixel array; (ii) outputting one or more event signals based at least in part on the generated photocurrent and a contrast threshold, wherein each of the one or more event signals indicates an event detected in the external scene; (iii) providing an ambient light signal indicating the ambient light level of the external scene; (iv) determining an optimal value for at least one of the contrast threshold of the hybrid imaging system or another sensor setting (e.g., which is expected to improve or maximize the image quality of the external scene) based at least in part on the one or more event signals and the ambient light signal; and (v) transmitting the determined optimal value to components of the hybrid imaging system in real time.

[0032] This technology is expected to offer several advantages. For example, different external scenes may require different hardware settings to enable the imaging system to achieve acceptable, improved, and / or highest image quality due to varying lighting conditions (e.g., bright and dim) and / or the causes of detected events (e.g., due to camera movement and object movement), as well as other differences. This technology is expected to provide hardware (e.g., event-driven circuitry hardware) adaptable to such different external scenes to, for example, improve or maximize the quality of images captured in or in such external scenes. As further discussed herein, embodiments of this technology can automatically and / or in real-time determine optimal sensor settings based at least in part on measurements of the captured external scene (e.g., event data, ambient light levels, etc.). Therefore, this technology is expected to automatically and / or in real-time adapt to different external scenes, thereby consistently providing high image quality.

[0033] B. Selected embodiments of image sensors and associated systems, devices, and methods

[0034] Figure 1The illustration depicts a stacked hybrid complementary metal-oxide-semiconductor (CMOS) image sensor (CIS) and event vision sensor (EVS) imaging system 100 (“System 100”) configured according to embodiments of the present technology. As shown in the depicted examples, System 100 includes a first die 102, a second die 104, and a third die 106 stacked and coupled together in a stacked chip configuration. In various examples, the first die 102, the second die 104, and the third die 106 are semiconductor dies comprising suitable semiconductor materials such as silicon. In one example, the first die 102 (which may also be referred to as the top die 102 of System 100) includes a pixel array 108. The third die 106 (which may also be referred to as the bottom die 106 of System 100) includes an image readout circuit 116 (also referred to herein as an image readout mixed-signal circuit system). The image readout circuit 116 can be coupled to the pixel array 108 of the top die 102 via a column-level connector 110 for normal image readout. In various instances, the column-level connector 110 for normal image readout is implemented from the column bit lines of the pixel array 108 using through-silicon vias (TSVs) extending between the top die 102 and the bottom die 106, and is routed through the second die 104.

[0035] In various instances, pixel array 108 is a two-dimensional (2D) array comprising a plurality of pixels (also referred to as “pixel units”), each pixel containing at least one photoelectric sensor exposed to incident light. As illustrated in the depicted examples, pixels are arranged in rows and columns to acquire image data of an external scene (e.g., people, places, objects, etc. within the external scene), which can then be used to reproduce images and / or videos of the external scene. As further discussed herein, each of the pixels may be a hybrid CIS / EVS pixel comprising a plurality of photoelectric sensors (e.g., photodiodes) configured to generate image charge in response to incident light. Pixel array 108 may operate in CIS-only mode, hybrid CIS / EVS mode, and / or EVS-only mode. When operating in CIS-only or hybrid CIS / EVS mode, appropriate portions of the acquired analog image charge data may be read out by image readout circuitry 116 in the bottom die 106 via column lines. In various instances, the image charge from each row of pixel array 108 can be read out in parallel via column lines by image readout circuit 116.

[0036] In various instances, the image readout circuitry 116 in the bottom die 106 includes amplifiers, analog-to-digital converter (ADC) circuitry, associated analog support circuitry, associated digital support circuitry, and so on, for normal image readout and processing. In some instances, the image readout circuitry 116 may also include an event-driven readout circuitry system, which will be described in more detail below. In operation, the generated analog image charge signal is read from the pixels of the pixel array 108 in the image readout circuitry 116, amplified, and converted into digital values. In some instances, the image readout circuitry 116 can read one line of image data at a time. In other instances, the image readout circuitry 116 can use a variety of other techniques (not illustrated) to read out image data, such as serial readout or simultaneous full parallel readout of all pixels. Image data can be stored or even manipulated by applying post-processing image effects (e.g., cropping, rotation, red-eye removal, brightness adjustment, contrast adjustment, etc.).

[0037] In the depicted example, the second die 104 (which may also be referred to as the intermediate die 104 of system 100) includes an event-driven sensing array 112 coupled to a pixel array 108 in the top die 102. In various examples, the event-driven sensing array 112 is coupled to the pixels of the pixel array 108 via one or more stacked pixel-level connectors (SPLCs) (e.g., hybrid joints) disposed between the top die 102 and the intermediate die 104. In one example, the event-driven sensing array 112 includes an array of event-driven circuitry. As will be discussed, in one example, according to an embodiment of the present technology, each of the event-driven circuits in the event-driven sensing array 112 is coupled to a corresponding one of a plurality of pixels in the pixel array 108 (e.g., via at least one hybrid joint between the top die 102 and the intermediate die 104) to asynchronously detect events occurring in light incident on the pixel array 108.

[0038] When operating in hybrid CIS / EVS or EVS-only mode, appropriate portions of the photosensors of each pixel in pixel array 108 can be used to track changes in the intensity of light incident on the photosensors from an external scene. Specifically, the photosensors can generate image charges (electrons or holes) or photocurrents in response to incident light from the external scene. The generated image can then be provided to coupled event-driven circuitry of event-driven sensing array 112 via an EVS connector, such as a hybrid junction. In some embodiments, each event-driven circuit includes: (i) a photocurrent-to-voltage converter coupled to one or more photosensors of the corresponding pixel to convert the photocurrent generated by the one or more photosensors into a voltage; and (ii) a differential circuit coupled to the photocurrent-to-voltage converter and configured to generate a differential signal or analog signal level (e.g., a logarithmic differential voltage relative to a reset time) at least in part based on the voltage. The event-driven sensing array 112 and / or event-driven peripheral circuitry 114 on the intermediate die 104 may further include a threshold comparison stage to determine and generate an event detection signal in response to an event asynchronously detected in incident light received from an external scene. For example, the threshold comparison stage generates the event detection signal when a change in the pixel signal detected at the output of the differential circuit relative to a reference pixel signal (in a positive or "brighter" direction, or in a negative or "darker" direction) is greater than a corresponding predetermined voltage threshold (also referred to herein as a "contrast threshold," "nominal contrast threshold," or "NCT"). It should be understood that the described event-driven readout circuitry is one exemplary embodiment for reading out event signals. Various embodiments of readout circuitry systems and readout schemes for event vision sensor pixels are well known. Therefore, for the sake of brevity and to avoid obscuring various aspects of the art, details regarding the circuitry system and readout techniques for event-driven circuitry are largely omitted herein.

[0039] As shown, the event-driven peripheral circuitry system 114 may be arranged around the periphery of the event-driven sensing array 112 in the intermediate die 104. In various embodiments, the event-driven peripheral circuitry system 114 may receive and process corresponding event detection signals. The depicted examples also illustrate a column-level connector 110 for normal image readout, which is routed through the intermediate die 104 between the top die 102 and the bottom die 106.

[0040] Figure 2 This is a schematic block diagram of a model training scheme 200 configured according to embodiments of the present technology. As described in further detail herein, embodiments of the present technology can determine (e.g., automatically and / or in real time) for event detection circuitry systems (e.g., included in...). Figure 1The event-driven circuitry in the event-driven sensing array 112 sets one or more optimal values ​​for one or more sensors. One such sensor setting that can be optimized is a contrast threshold used by a threshold comparison stage. (See above reference...) Figure 1 As discussed, when the change of the detected pixel signal (e.g., at the output of the differential circuit) relative to the reference pixel signal is greater than the corresponding NCT, the threshold comparison stage can generate an event detection signal. Figure 2 The diagram illustrates how to use a learning model or learning algorithm to optimize the value of NCT.

[0041] Model training scheme 200 may include training dataset 210, hybrid sensor model 220, function 230, fusion model 240, and image quality (IQ) processor 250. Training dataset 210 may contain images and / or videos used to train hybrid sensor model 220, as discussed in further detail herein. For example, training dataset 210 may be derived from synthetic datasets (e.g., Blender, Neural Radiation Field (NeRF), and / or similar), real images and / or videos (e.g., captured by a high-speed camera), or combinations thereof. Training dataset 210, along with an initial nominal contrast threshold (NCT'), may be input into hybrid sensor model 220. Hybrid sensor model 220 may then be run to detect events (if present) in training dataset 210 based on the initial NCT'.

[0042] In some embodiments, to obtain measurements of the image / video quality and / or EVS data of the captured external scene, the hybrid sensor model 220 may use Video Multi-Method Evaluation Fusion (VMAF) video quality metric or another suitable video quality evaluation metric (e.g., VideoDVP). (Visual Difference Predictor, ColorVideoDVP). For example, fusion model 240 may generate an improved image stream 242, such as a deblurred image stream, an image stream corrected for rolling shutter distortion, an interpolated image stream, and / or the like. In some embodiments, fusion model 240 uses event data (e(t)), ambient light (a(t)), and / or image frames (c(t)) output from hybrid sensor model 220 as input. Continuing with the above example, training dataset 210 may be input to IQ processor 250 to generate benchmark data 252. In some embodiments, IQ processor 250 may be configured to: (i) adjust resolution, color space, and / or the like; and / or (ii) perform time downsampling on training dataset 210. Furthermore, the improved image stream 242 may be compared with the benchmark data 252 that can be used as a reference to generate a VMAF score. Thus, the measured image quality may be represented by the following equation (1):

[0043]

[0044] Where IQ is an image quality metric, m is the improved image stream 242 output by fusion model 240, e represents the event signal, a represents the ambient light signal, c represents the frame signal, GT is the reference data 252 output by IQ processor 250, and VMAF is the VMAF score obtained by comparing m with GT. The obtained VMAF score can represent the predicted subjective image / video quality of the captured external scene. Additional details known in this document that are associated with the VMAF video quality metric are omitted to avoid unnecessarily obscuring the characteristics of this technique. It should be understood that in other embodiments, metrics other than VMAF may be used to obtain measurements of the image / video quality and / or EVS data of the captured external scene.

[0045] In some embodiments, once the image quality metric is obtained, the hybrid sensor model 220 can use function 230 to determine the optimized nominal contrast threshold (NCT”). For example, function 230 can be used to determine the value of the NCT that optimizes (e.g., improves or maximizes) the image quality metric IQ. Furthermore, function 230 can set and output the value of the NCT as the optimized NCT”. Therefore, the calculation of the optimized NCT” can be represented by the following equation (2):

[0046]

[0047] Where f is function 230. In some embodiments, function 230 is a learning algorithm, such as a support vector machine (SVM) learning algorithm, a convolutional neural network (CNN) learning algorithm, and / or the like. Once the optimized NCT is determined, the optimized NCT can be passed to the event-driven circuit system. The event-driven circuit system can then use the optimized NCT to detect future events in the external scene (if they exist).

[0048] In some embodiments, the optimized NCT is determined using event signals obtained between individual CIS frames along with ambient light signals. EVS signals are typically obtained more frequently than CIS signals (e.g., EVS signals may be obtained at approximately 1,000 to 10,000 frames per second, while CIS signals may be obtained at approximately 60 to 100 frames per second). Therefore, embodiments of this technology are expected to determine and deliver the optimized NCT with very low latency, thereby enabling real-time hardware parameter optimization.

[0049] Finding an optimized NCT is expected to improve the event sensing capability of the event-driven circuitry system of the image sensor. As discussed above, the sensitivity and accuracy of the event-driven circuitry system in detecting changes or events in the captured external scene are determined at least in part by the contrast threshold used in the comparison stage of the event-driven circuitry system. Therefore, if the contrast threshold is set too low, the event-driven circuitry system may become oversensitive, leading to false positives caused by noise, minor fluctuations, and / or irrelevant background changes. Conversely, if the contrast threshold is too high, it may miss genuine events, leading to false negatives and failure to capture important occurrences. Therefore, determining an optimized NCT can be considered as achieving the optimal balance between sensor sensitivity and accuracy, which is particularly important in applications such as surveillance, autonomous navigation, scientific monitoring, and / or the like. As discussed above, Figure 2 The model training scheme 200 can be used to train the hybrid sensor model 220 to develop and use the function 230 to identify optimized NCT for a given scenario.

[0050] Figure 3 This is a schematic block diagram of a hybrid imaging system 300 (“System 300”) configured according to embodiments of the present technology. It should be understood that... Figure 3 System 300 can provide Figure 1 The system 100 (or a portion thereof) and / or examples of other imaging systems configured according to various embodiments of the present technology, and the similarly named and numbered elements described above are similarly coupled and function in the following text. Specifically, Figure 3 The diagram illustrates an exemplary hardware architecture that can be used to implement embodiments of this technology. System 300 may include a hybrid CIS / EVS sensor core 320, a sensor processor 360, and an output interface 370.

[0051] The hybrid CIS / EVS sensor core 320 may include a pixel array 308 (e.g., Figure 1 The pixel array 308 and multiple control circuits include column control circuit 322, row control circuit 324, and setting control circuit 326. Each of the control circuits 322, 324, and 326 can be operatively coupled to the pixel array 308 and can be configured to control various aspects of its operation (e.g., column readout, row readout, and configuration of the pixel array 308 between CIS-only, hybrid CIS / EVS, and / or EVS-only modes).

[0052] The hybrid CIS / EVS sensor core 320 may also include circuitry for processing EVS signals or information, such as event-driven circuitry 330, a noise reducer / filter 332, a row and / or column scanner 334, and an event sensing processor (ESP) 336. (See above reference) Figure 1As described, event-driven circuitry 330 is operatively coupled to pixel array 308 and configured to receive photocurrent from said pixel array to determine events (if present) in the captured external scene. Noise reduction / filter 332 is operatively coupled to event-driven circuitry 330 and configured to reduce noise from event signals and / or otherwise filter event signals. Row and / or column scanner 334 is operatively coupled to noise reduction / filter 332 and configured to retrieve event signals row-by-row and / or column-by-column. ESP 336 is operatively coupled to row and / or column scanner 334 and configured to further process the retrieved event signals to detect events. The resulting event signals may be, for example, generated by… Figure 2 The fusion model 240 uses event data (e(t)).

[0053] Additionally, the hybrid CIS / EVS sensor core 320 may include one or more ambient light monitors 340, which are operatively coupled to the pixel array 308 and configured to measure ambient light and generate a corresponding ambient light signal. In some embodiments, the ambient light monitors 340 may be arranged around and / or between individual pixels and / or groups of pixels in the pixel array 308. The generated ambient light signal may be, for example, generated by… Figure 2 The fusion model 240 uses ambient light (a(t)).

[0054] The hybrid CIS / EVS sensor core 320 may further include circuitry for processing CIS signals or information, such as an amplifier 350, a gain control circuit 352, an ADC circuit 354, a black level calibration circuit 356, and a digital gain circuit 358. The amplifier 350 may be operatively coupled to the pixel array 308, the gain control circuit 352 may be operatively coupled to the amplifier 350, and the amplifier 350 and gain control circuit 352 may be configured to control the gain control or other forms of amplification of the CIS signal. The ADC circuit 354 may be operatively coupled to the amplifier 350 and configured to convert the analog CIS signal into a digital CIS signal. The black level calibration circuit 356 may be operatively coupled to the ADC circuit 354 and configured to adjust and compensate the baseline signal level (black level) to ensure accurate representation of pixel values ​​under low light or dark conditions. The digital gain circuit 358 may be operatively coupled to the black level calibration circuit 356 and configured to amplify the digital CIS signal to achieve the desired brightness and dynamic range, thereby ensuring appropriate signal scaling for further processing. CIS signals can be, for example, derived from... Figure 2 The fusion model 240 uses the image frame (c(t)).

[0055] Sensor processor 360 can be operatively coupled to hybrid CIS / EVS sensor core 320 and may include buffer 362, digital signal processor (DSP) 364, decoder 366, and sensor controller 368. Buffer 362, DSP 364, and decoder 366 can be configured to further process event data (e(t)), ambient light data (a(t)), and CIS data (c(t)) received from hybrid CIS / EVS sensor core 320. Furthermore, sensor controller 368 can be configured to use the received event data (e(t)), ambient light (a(t)), CIS data (c(t)), and / or other received data (e.g., noise data) to determine optimized values ​​for sensor settings. For example, [the following is an example of a sensor controller 368]. Figure 3 and Figure 2 Relatedly, the sensor controller 368 may implement function 230 to find an NCT value that optimizes (e.g., improves, maximizes) the image quality.

[0056] Output interface 370 may include a Mobile Industrial Processor Interface (MIPI) 372 and / or other suitable interface components configured to, for example, transmit data to and / or from a host device. In some embodiments, optimizations are performed in addition to or instead of (e.g., using...) Figure 2 The sensor controller 368 (function 230) handles / executes this optimization by the host device, and the MIPI 372 can manage data communication to and from the host device.

[0057] System 300 may further include a data bus 380, a timing generator and system control logic circuitry 382, ​​a serial interface 384, a control register set 386, and registers 388. The data bus 380 may extend across the hybrid CIS / EVS sensor core 320 and the sensor processor 360. The functions of the serial interface 384, control register set 386, and registers 388, as known in this art, are omitted here to avoid unnecessarily obscuring aspects of the art. As shown, the data bus 380 is capable of: (i) receiving data signals from the sensor controller 368, the timing generator and system control logic circuitry 382, ​​the control register set 386, and registers 388; and (ii) transmitting data signals to each of the control circuits 322, 324, 326, the gain control circuitry 352, the DSP 364, the decoder 366, and the sensor controller 368. Therefore, once the sensor controller 368 (and / or the host device or other device) determines the optimal value of one or more sensor hardware settings, the optimal value can be passed to the relevant system components (e.g., setting control circuit 326), and the relevant system components can update their hardware settings to incorporate the optimal value.

[0058] In some embodiments, optimization is performed multiple times (e.g., in a feedback loop) so that sensor hardware parameters are continuously optimized periodically, periodically, or otherwise (e.g., according to predefined intervals, in response to predetermined events, etc.). For example, optimization may be performed once per CIS frame. In such embodiments, the NCT value may remain unchanged during event accumulation, which can be advantageous for performing operations such as deblurring. In another instance, optimization may be performed more than once per CIS frame. In such embodiments, the imaging system may exhibit improved event detection, object detection, and / or the like. In some embodiments, optimization is performed before image and / or event capture (e.g., during standby or idle modes).

[0059] Figure 4 The diagram illustrates an event-driven circuit 430 configured according to an embodiment of the present technology. It should be understood that... Figure 4 The event-driven circuit 430 can be included in Figure 1 The event-driven circuitry (or a portion thereof) in the event-driven sensing array 112 Figure 3 Examples of event-driven circuitry 330 and / or other circuits configured according to various embodiments of the present technology, and elements with similar naming and numbering described above are similarly coupled and function in the following text. Specifically, Figure 4 The illustrations illustrate various other sensor hardware settings that can be optimized according to embodiments of the present technology, such as in addition to or instead of the nominal contrast threshold.

[0060] As shown, the event-driven circuit 430 may include a photocurrent-to-voltage converter 432, a differential circuit 434, a threshold comparator stage 436, and logic and handshake circuitry 438. The photocurrent-to-voltage converter 432 may be coupled to one or more photosensors 412 via an EVS transistor 414 and / or a stacked pixel-level interconnect (SPLC), and the photocurrent-to-voltage converter 432 may be configured to convert the photocurrent generated by the one or more photosensors 412 into a voltage. In some embodiments, the photocurrent-to-voltage converter 432 may be or include a logarithmic amplifier. The differential circuit 434 may be coupled to the photocurrent-to-voltage converter 432 and may be configured to: (a) store a reference voltage at a reset time; and (b) thereafter generate a differential signal or analog signal level (e.g., a logarithmic differential voltage relative to the reset time) based at least in part on the voltage from the photocurrent-to-voltage converter 432. Therefore, the differential circuit 434 may include memory elements (e.g., one or more capacitors) to store the reference voltage and / or the differential signal.

[0061] In some embodiments, threshold comparison stage 436 includes an up event comparator and a down event comparator coupled in parallel between differential circuit 434 and logic and handshake circuit 438. In some embodiments, threshold comparison stage 436 is coupled to multiple differential circuits (e.g., multiple event-driven circuits). Threshold comparison stage 436 may be configured to determine and generate an event detection signal in response to an event asynchronously detected in incident light received from an external scene. For example, when the absolute value of the output signal of differential circuit 434 is greater than a nominal contrast threshold, threshold comparison stage 436 may assert an event detection signal, thereby indicating that the corresponding pixel (e.g., photosensitive sensor 412) has detected an event in the external scene. The event detection signal generated by threshold comparison stage 436 may be output to logic and handshake circuit 438. Furthermore, logic and handshake circuit 438 may be configured to process the event detection signal. Additional details regarding the photocurrent-to-voltage converter, differential circuitry, threshold comparator stage, and logic and handshake circuitry are provided in U.S. Patent Application No. 17 / 875,244, filed July 27, 2022, entitled “Low Power Event Driven Pixels with Active Differential Detection Circuitry and Reset Control Circuits for the Same,” the disclosure of which is incorporated herein by reference in its entirety.

[0062] like Figure 4As shown, the photocurrent-to-voltage converter 432 may include a coupling capacitor 433, and the differential circuit 434 may include a current source 435. In some embodiments, the coupling capacitor 433 is a variable capacitor whose capacitance may vary, for example, in response to a control signal. Similarly, the current source 435 may be configured to supply a variable bias current (e.g., to the source follower transistor of the differential circuit 434). In operation, the capacitance of the coupling capacitor 433 and / or the current supplied by the current source 435 may affect the noise level observed in the event-driven circuit 430. Furthermore, the capacitance of the coupling capacitor 433 may affect the bandwidth of the event and the pixel response latency, and the current supplied by the current source 435 may affect the bandwidth of the event signal output. Thus, these hardware settings may affect, for example, the event rate measured by the event counter / analyzer circuitry (e.g., reducing noise can reduce false positives). Therefore, in addition to optimizing the nominal contrast threshold, or alternatively to optimizing the nominal contrast threshold, additional ways to improve image quality may include optimizing the capacitance of coupling capacitor 433 and / or the value of the current supplied by current source 435. For example, increasing the capacitance of coupling capacitor 433 and / or increasing the current supplied by current source 435 is expected to reduce the noise observed in event-driven circuitry 430, thereby reducing the average event rate. In some embodiments, as discussed above, feedback loops may be used to continuously optimize these (and / or other) hardware settings.

[0063] Figure 5 This is a schematic block diagram of the event detection portion of a hybrid CIS / EVS imaging system 500 (“System 500”) configured according to an embodiment of the present technology. It should be understood that... Figure 5 System 500 can provide Figure 1 The system 100 (or a portion thereof) and / or instances of other imaging systems configured according to various embodiments of the present technology, and the similarly named and numbered elements described above are similarly coupled and function in the following text. Figure 5 The diagram illustrates an exemplary hardware architecture that can be used to process EVS data according to embodiments of the present technology. System 500 may include pixel array 508, filtering and latching circuitry 530, noise reduction / noise counter circuitry 532, event counter / analyzer circuitry 534, row scanner 523, column scanner 535, row scanner manager 522, and column scanner manager 524.

[0064] Pixel array 508 may include a plurality of pixels arranged in rows and columns. Filtering and latching circuitry 530 may receive event signals from individual columns of pixel array 508. Noise reduction / counter circuitry 532 may count noise events in the event signals and obtain corresponding noise rate information from the event signals. In some embodiments, noise reduction / counter circuitry 532 includes a buffer memory for event mapping. Event counter / analyzer circuitry 534, row scanner 523, and / or column scanner 535 may count the number of events detected in each row and / or column during a single event frame scan. For example, event counter / analyzer circuitry 534 may generate event rate information in tabular form, the table listing the number of events counted in each column and / or row. Event counter / analyzer circuitry 534 may also find the standard deviation (sigma (σ)) of the event rate, for example, by determining or using local event rate changes (e.g., differences in event counts between adjacent columns and / or rows).

[0065] Therefore, system 500 can calculate or provide various information fragments, such as the event rate for each column / row, the average event rate across multiple columns / rows, the standard deviation of the event rates between adjacent columns / rows, and noise rate information. As discussed earlier above, associated ambient light data can also be provided. These information fragments can be provided to downstream system components (e.g., Figure 3 The sensor controller 368) is used to execute one or more optimized sequences, for example, using Figure 2 Function 230. Alternatively, see the following reference: Figure 6 In further detail, System 500 can optimize sensor settings by using the various information fragments described herein and by using lookup tables to determine the optimal values.

[0066] Figure 6 Table 600 lists various scene content parameters according to embodiments of the present technology. As further discussed herein, the values ​​listed in Table 600 may form part of a reference value and / or lookup table that can be used in conjunction with embodiments of the present technology. As shown in the first column of Table 600, the external scene may have various lighting conditions, such as a high-light scene, a low-light scene, a non-high dynamic range (HDR) scene, or an HDR scene. Events detected in high-light scenes and low-light scenes can be distinguished by ambient light levels (e.g., high ambient light level and low ambient light level, respectively). Events detected in each of the non-HDR and HDR scenes can be further classified by specific scene conditions, such as whether the event was detected in a static scene due to camera movement or due to object movement.

[0067] Table 600 further lists (i) time variance and (ii) spatial and temporal differences. (See above for reference.) Figure 5 As discussed above, temporal variance can be correlated with the standard deviation of the event rate over time. Spatial and temporal variances can be correlated with (i) the standard deviation of local event rate changes between neighboring rows and / or columns and (ii) the standard deviation of event counts over time, respectively, as also mentioned above. Figure 5 This has been discussed. At a high level, spatial information and ambient light levels are expected to distinguish scenes with different lighting conditions, and temporal information is expected to distinguish whether an event is detected due to camera motion or object motion. For example, as shown in Table 600, spatial difference is expected to be at a medium level for non-HDR scenes and at a large level for HDR scenes. Similarly, ambient light levels are expected to be low to medium for non-HDR scenes and high for HDR scenes. Likewise, as shown in Table 600, regardless of whether the event is detected in a non-HDR or HDR scene, temporal difference is expected to be small for events detected due to camera motion and at a medium to large level for events detected due to object motion.

[0068] Figure 6 Table 600 illustrates the various scene types supported by sensor hardware settings optimized according to embodiments of the present technology. (See also:) Figure 5 and 6 In some embodiments, various information fragments obtained via system 500 and table 600 can be used to optimize one or more hardware settings. For example, once spatial and / or temporal information is obtained, it can be compared with the “buckets” (e.g., low level, medium level, high level) listed in table 600 to determine whether the detected scene is a high-lighting scene, a low-lighting scene, a non-HDR scene, or an HDR scene, and to determine whether the detected event originates from camera motion or object motion. Once the lighting conditions and / or specific scene conditions are determined, a lookup table with specific hardware optimization values ​​(not shown in the figures) can be referenced to determine the optimal values ​​of one or more sensor settings suitable for a given scene and / or condition. For example, a low-lighting scene with a higher noise level than a high-lighting scene can be expected to be optimized by the capacitance of coupling capacitor 433 and / or by current source 435 ( Figure 4 This is associated with a lower optimized value of the supplied current.

[0069] Therefore, this article references Figure 5 and 6 The lookup table method discussed above can be used as a reference. Figure 2Alternatives (or supplements) to the described function 230. For example, different scenes and / or situations may be associated with different predetermined optimal values ​​for the nominal contrast threshold. Compared to using function 230, the lookup table method may require less computation, thus using fewer imaging system computational resources. Compared to the lookup table method, using function 230 can produce more accurate and customized optimized values. In some embodiments, both function 230 and the lookup table method may be employed, such that the optimized values ​​determined by each can be used for mutual verification, averaging, and / or similar operations.

[0070] Figure 7 This is a flowchart illustrating a method 780 for operating an imaging system according to an embodiment of the present technology. The imaging system may be... Figure 1 The system 100 (or a portion thereof) and / or examples of other imaging systems configured according to various embodiments of the present technology. Method 780 is illustrated as a series of blocks or steps 781 to 787. All or a subset of one or more of steps 781 to 787 may be performed in accordance with the above description and / or the following description.

[0071] At box 781, method 780 may optionally involve training a hybrid sensor model (e.g., Figure 2 The hybrid sensor model 220 begins here. (See reference above.) Figure 2 In detail, a training dataset and initial NCT values ​​(NCT') can be input into a hybrid sensor model, which can then be run to detect events (if any). Image / video quality metrics, such as VMAF, can be computed by comparing the image stream from the hybrid sensor model with benchmark data (e.g., output from an image quality processor). The hybrid sensor model can then use a function (e.g., a learning algorithm) to determine an optimized NCT value (NCT') by finding, for example, an NCT value that improves or maximizes the image / video quality metric. Therefore, the hybrid sensor model can be trained to find optimal system parameter values.

[0072] At block 782, method 780 may continue (or optionally begin) by receiving event data and ambient light intensity from the external scene. The event data may include a history of detected events and their timestamps (if present). Ambient light intensity may indicate the brightness level and / or other details of ambient light present in the external scene (e.g., detected by an ambient light sensor, such as an ambient light sensor located at multiple different segments of a pixel array). In some embodiments, via Figure 3 The system Figure 4 Event-driven circuit 430 Figure 5 The system receives event data for operation of 500 and / or similar systems.

[0073] At box 783, method 780 can continue by determining one or more scene content parameters based on the received event data and ambient light intensity. (See above reference.) Figure 3 and 6 As discussed above, one or more scene content parameters may include event rates, temporal variance in event data, spatial / temporal differences in event data, noise information, and / or similar parameters. These scene content parameters can help identify the nature of any detected event, the external context, etc.

[0074] At box 784, method 780 can continue by determining lighting conditions and / or scene conditions based on one or more determined scene content parameters and / or ambient light intensity. Lighting conditions can be a high-light scene, a low-light scene, a non-HDR scene, or an HDR scene. Scene conditions can indicate the cause of any detected event and can be a static scene with camera motion or a scene with object motion. (See above reference...) Figure 6 As discussed, lighting conditions and / or scene situations can be identified based on whether each of the scene content parameters and / or ambient light intensity is low / small, medium, or high / large. In some embodiments, box 784 is omitted and method 780 may alternatively use a hybrid sensor model (e.g., the model trained at box 781) to determine the optimal value more directly based on event data and ambient light intensity.

[0075] At block 785, method 780 can continue by determining optimal system parameters. In some embodiments, the optimal system parameters are determined by a model. For example, if the step at block 781 has been performed, the trained hybrid sensor model can use a function (e.g., a learning algorithm) to determine the optimal NCT value. In some embodiments, the optimal system parameters are determined at least in part based on the determined lighting conditions and / or scene conditions. For example, as referenced above... Figure 6 The discussion can be referenced in a lookup table that associates lighting conditions and / or scene circumstances with one or more optimal system parameters.

[0076] At box 786, method 780 can continue by adjusting the system parameters to the determined optimal system parameters. For example, as referenced above... Figure 3 As discussed, once the sensor controller 368 (and / or the host device or other device) determines optimal values ​​for one or more sensor hardware settings, these optimal values ​​can be passed to relevant system components (e.g., setting control circuitry 326), and the relevant system components can update their hardware settings to incorporate the optimal values. In some embodiments, method 780 can return to block 782 to perform optimization multiple times (e.g., in a feedback loop), such that the sensor hardware parameters can be continuously optimized periodically, periodically, or otherwise (e.g., according to predefined intervals, in response to predetermined events, etc.).

[0077] At box 787, method 780 may continue by capturing image data of the external scene. Compared to image data captured without using this technique, the captured image data is expected to (i) have higher image / video quality and / or (ii) contain event data with fewer false positives or false negatives.

[0078] Although discussed and illustrated in a specific order Figure 7 Method 780 includes steps 781 to 787, but method 780 is not so limited. In other embodiments, all or a subset of one or more of steps 781 to 787 of method 780 may be performed in a different order. Furthermore, those skilled in the art will recognize that the illustrated method 780 can be modified while remaining within these and other embodiments of the present technology. For example, in some embodiments, Figure 7 All or a subset of one or more of steps 781 to 787 of method 780 illustrated in the figure may be omitted and / or repeated. As another example, method 780 may include, except... Figure 7 One or more additional steps beyond those shown in the diagram.

[0079] C. in conclusion

[0080] The above detailed description of embodiments of this technology is not intended to be exhaustive or to limit the technology to the precise forms disclosed above. While specific embodiments and examples of this technology have been described above for illustrative purposes, those skilled in the art will recognize that various equivalent modifications can be made within the scope of this technology. For example, although the steps are presented in a given order above, alternative embodiments may perform the steps in a different order. Furthermore, the various embodiments described herein can be combined to provide further embodiments.

[0081] Based on the foregoing, it will be understood that, for illustrative purposes, specific embodiments of the present technology have been described herein, but well-known structures and functions have not been shown or described in detail to avoid unnecessarily obscuring the description of embodiments of the present technology. In the event of any conflict between any material incorporated herein by reference and this disclosure, this disclosure shall prevail. Where the context permits, singular or plural terms may also contain plural or singular terms, respectively. Furthermore, unless the word “or” is expressly limited to referring only to a single item excluding other items when referring to a list of two or more items, its use in this list shall be construed as including (a) any single item in the list, (b) all items in the list, or (c) any combination of items in the list. Additionally, as used herein, the phrase “and / or” in “A and / or B” means only A, only B, and both A and B. Furthermore, throughout the text, the terms “comprising,” “including,” “having,” and “with” are used to mean that at least the stated features are included, without excluding any larger number of identical features and / or other features of additional types. Additionally, as used herein, the phrases “based on,” “depending on,” “as a result of,” and “in response to” should not be construed as referring to a closed set of conditions. For example, an exemplary step described as “based on condition A” may be based on both condition A and condition B without departing from the scope of this disclosure. In other words, as used herein, the phrase “based on” should be interpreted in the same manner as the phrases “based at least in part on” or “based at least partially on.” Moreover, the terms “connection” and “coupling” are used interchangeably herein and refer to both direct and indirect connection or coupling. For example, where the context permits, a component A being “connected” or “coupled” to a component B can mean (i) A being directly “connected” or directly “coupled” to B and / or (ii) A being indirectly “connected” or indirectly “coupled” to B.

[0082] Based on the foregoing, it will also be understood that various modifications can be made without departing from this disclosure or technology. For example, those skilled in the art will understand that various components of this technology can be further divided into sub-components, or various components and functions of this technology can be combined and integrated. Additionally, in other embodiments, specific aspects of the technology described in the context of a particular embodiment can be combined or eliminated. Furthermore, while advantages associated with specific embodiments of this technology have been described in the context of those embodiments, other embodiments may also exhibit such advantages, and not all embodiments necessarily need to exhibit such advantages to fall within the scope of this technology. Therefore, this disclosure and related technologies may encompass other embodiments not explicitly shown or described herein.

Claims

1. An imaging system comprising: A pixel array comprising a plurality of pixels arranged in a plurality of rows and a plurality of columns, each of the plurality of pixels comprising a photoelectric sensor configured to generate a photocurrent in response to incident light received from an external scene; An event detection circuitry system operatively coupled to the pixel array, wherein the event detection circuitry system is configured to receive the photocurrent from one or more of the photoelectric sensors and output one or more event signals based at least in part on the received photocurrent and a contrast threshold, wherein each of the one or more event signals indicates an event detected in the external scene. An ambient light monitor operatively coupled to the pixel array, wherein the ambient light monitor is configured to output an ambient light signal indicating the ambient light level of the external scene; as well as A sensor controller operatively coupled to each of the event detection circuitry system and the ambient light monitor, wherein the sensor controller is configured to: Receives one or more event signals from the event detection circuit system and an ambient light signal from the ambient light monitor. The contrast threshold or an optimized value set by another sensor for the event detection circuitry system is determined at least in part based on the one or more event signals and the ambient light signal. This optimized value can be used by the imaging system to improve the image quality of one or more images of the external scene. The optimized value is then passed to the event detection circuit system.

2. The imaging system of claim 1, wherein the sensor controller is configured to determine the optimized value for the contrast threshold.

3. The imaging system of claim 1, wherein the sensor controller is configured to use a learning algorithm to determine the optimized value.

4. The imaging system of claim 1, further comprising (a) an event counter or analyzer and (b) a row scanner or column scanner, each operatively coupled to the event detection circuitry, wherein (a) the event counter or analyzer and (b) the row scanner or column scanner are configured to: Receive the one or more event signals from the event detection circuit system; and Spatial and / or temporal event information associated with the external scene is determined, at least in part, based on one or more event signals.

5. The imaging system of claim 1, wherein the sensor controller is configured to determine the optimized value based at least in part on one or more event occurrence rates, each of the one or more event occurrence rates corresponding to a corresponding row of the plurality of rows or a corresponding column of the plurality of pixels.

6. The imaging system of claim 1, wherein the sensor controller is configured to determine the optimized value based at least in part on the average of two or more event occurrence rates, each of the two or more event occurrence rates corresponding to a corresponding row of the plurality of rows or a corresponding column of the plurality of pixels.

7. The imaging system of claim 1, wherein the sensor controller is configured to determine the optimized value based at least in part on the standard deviation of a plurality of event occurrence rates, each of the plurality of event occurrence rates corresponding to a corresponding row of the plurality of rows or a corresponding column of the plurality of columns of the plurality of pixels.

8. The imaging system of claim 1, further comprising a noise reduction unit operatively coupled to the event detection circuitry, wherein the sensor controller is configured to determine the optimized value based at least in part on noise rate information generated by the noise reduction unit.

9. The imaging system of claim 1, wherein the sensor controller is configured to determine the optimized value based at least in part on complementary metal-oxide-semiconductor image sensor information, the complementary metal-oxide-semiconductor image sensor information being generated at least in part based on the photocurrent generated by at least a subset of the plurality of pixels.

10. The imaging system according to claim 1, wherein: The event detection circuit system includes: (i) a photocurrent-to-voltage converter with a coupling capacitor having a variable capacitance; (ii) a differential circuit with a current source configured to generate a bias current; or (iii) a combination thereof. The sensor controller is configured to determine the optimized value set for the other sensor in the event detection circuitry system; and To determine the optimized value set for the other sensor of the event detection circuit system, the sensor controller is configured to determine the optimized value for the variable capacitor and / or the bias current.

11. The imaging system of claim 1, wherein the sensor controller is configured to determine, between complementary metal-oxide-semiconductor image sensor frames, the contrast threshold for the event detection circuitry system or the optimized value set by the other sensor.

12. The imaging system according to claim 1, wherein: The sensor controller is configured to transmit the optimized value to the event detection circuitry system in real time; and / or The event detection circuitry is configured to adjust the contrast threshold of the event detection circuitry or the setting of another sensor to the optimized value in real time in response to the sensor controller transmitting the determined optimized value to the event detection circuitry, thereby forming a feedback loop with the sensor controller.

13. The imaging system of claim 1, wherein the image quality of the one or more images of the external scene is at least partially based on a video quality assessment metric.

14. The imaging system of claim 1, wherein the sensor controller is configured to automatically determine and transmit an optimized value set for the contrast threshold or the other sensor at predetermined intervals or in response to the occurrence of one or more predetermined events.

15. A sensor processor for an imaging system, the sensor processor comprising: A sensor controller operatively coupled to an event detection circuitry of the imaging system and an ambient light monitor of the imaging system, wherein the sensor controller, when coupled to the event detection circuitry and the ambient light monitor, is configured to: Receive one or more event signals from the event detection circuit system and an ambient light signal from the ambient light monitor. The contrast threshold or an optimized value set for another sensor is determined, at least in part, based on the one or more event signals and the ambient light signal, for the event detection circuitry system. This optimized value can be used by the imaging system to improve the image quality of one or more images of the external scene. The optimized value is then passed to the event detection circuit system.

16. The sensor processor of claim 15, wherein the sensor controller is configured to determine the optimized value using a support vector machine learning algorithm or a convolutional neural network learning algorithm.

17. The sensor processor of claim 15, wherein the sensor controller is configured to determine the optimized value based at least in part on one or more event occurrence rates, each of the one or more event occurrence rates corresponding to a corresponding row or column of a plurality of pixels operatively coupled to the event detection circuitry system.

18. The sensor processor of claim 15, wherein the sensor controller is configured to determine the optimized value based at least in part on the average of two or more event occurrence rates, each of the two or more event occurrence rates corresponding to a corresponding row or column of a plurality of pixels operatively coupled to the event detection circuitry system.

19. The sensor processor of claim 15, wherein the sensor controller is configured to determine the optimized value based at least in part on the standard deviation of a plurality of event occurrence rates, each of the plurality of event occurrence rates corresponding to a corresponding row or column of a plurality of pixels operatively coupled to the event detection circuitry system.

20. A method for operating an imaging system, the method comprising: In response to incident light received from an external scene, a photoelectric sensor containing multiple pixels in a pixel array is used to generate a photocurrent; One or more event signals are output, at least in part, based on the photocurrent and contrast threshold, wherein each of the one or more event signals indicates an event detected in the external scene; Generate an ambient light signal that indicates the ambient light level of the external scene; The contrast threshold or an optimized value set by another sensor for the imaging system is determined at least in part based on the one or more event signals and the ambient light signal, the optimized value being usable by the imaging system to improve the image quality of one or more images of the external scene; and The optimized value is passed to a component of the imaging system to adjust the contrast threshold or the setting of another sensor.

21. The method of claim 20, wherein determining the optimized value further comprises determining the optimized value based at least in part on: (i) one or more event occurrence rates, each event occurrence rate corresponding to a corresponding row or column of the plurality of pixels; (ii) the average of the plurality of event occurrence rates; and / or (iii) the standard deviation of the plurality of event occurrence rates.

22. The method of claim 20, further comprising: Adjust the contrast threshold or the other sensor setting to the optimized value; and While the contrast threshold or the other sensor setting is adjusted to the optimized value, one or more images of the external scene are captured.

Images