An image processing method, apparatus, device and medium

Abstract

This application discloses an image processing method, apparatus, device, and medium, relating to the field of image processing technology. The method includes: acquiring the temperature state of an image sensor; and when the temperature state of the image sensor indicates that the temperature of the image sensor is higher than a preset threshold, activating thermal noise calibration parameters. In summary, this application's activation of thermal noise calibration parameters at high temperatures can effectively reduce image jitter noise and improve motion blur, resulting in better visual effects.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and in particular to an image processing method, apparatus, device, and medium. Background Technology

[0002] As ambient temperature rises, some image sensors, due to design and manufacturing processes, experience dark current drift, resulting in noise and color cast in the image, also known as thermal noise. This problem is more pronounced in low-light scenes; the higher the temperature, the more severe the image jitter noise. Traditional noise reduction methods, such as limiting the overall gain or adjusting temporal parameters, have significant drawbacks: limiting the overall gain easily leads to insufficient brightness and a darkened image in low light; adjusting temporal parameters based on OB (Optical Black) register values ​​exacerbates motion blur.

[0003] Therefore, how to reduce screen jitter noise and improve motion blur to enhance the visual effect of the image under high temperature conditions is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide an image processing method, apparatus, device, and medium that can effectively reduce image jitter noise and improve motion blur, resulting in a better visual effect. The specific solution is as follows: In a first aspect, this application discloses an image processing method, comprising: Acquire the temperature status of the image sensor; When the temperature status of the image sensor indicates that the temperature of the image sensor is higher than a preset threshold, the thermal noise calibration parameter is enabled.

[0005] Optionally, when performing the step of enabling thermal noise calibration parameters, the following steps are also performed: Enable the dead pixel correction function of the image sensor.

[0006] Optionally, after acquiring the temperature state of the image sensor, the method further includes: When the temperature status indicator of the image sensor indicates that the temperature of the image sensor is not higher than a preset threshold, the general noise calibration parameter is enabled. The general noise calibration parameter is different from the thermal noise calibration parameter.

[0007] Optionally, when performing the step of enabling common noise calibration parameters, the following steps are also performed: Disable the dead pixel correction function of the image sensor.

[0008] Optionally, acquiring the temperature state of the image sensor includes: Obtain the register value of a target register in the image sensor, the target register being used to indicate the temperature of the image sensor; When the register value of the target register is higher than a first threshold, the temperature status of the image sensor indicates that the temperature of the image sensor is higher than a preset threshold.

[0009] Optionally, the calibration process for the thermal noise calibration parameters includes: When the temperature is higher than a preset threshold and the bad pixel correction function of the image sensor is turned off, the original sample image data output by the image sensor is acquired. Noise calibration was performed on the original image data of the samples to obtain thermal noise calibration parameters.

[0010] Optionally, before acquiring the temperature state of the image sensor, the method further includes: Acquire the raw image data output by the image sensor; When the original image data is acquired under low irradiance conditions, the following step is performed: obtain the temperature status of the image sensor.

[0011] Optionally, when the digital gain and / or analog gain of the original image data is greater than the corresponding gain threshold, the original image data is data acquired under low irradiance conditions.

[0012] Secondly, this application discloses an image processing apparatus, comprising: Temperature status acquisition module, used to acquire the temperature status of the image sensor; The calibration parameter enabling module is used to enable thermal noise calibration parameters when the temperature status indicator of the image sensor indicates that the temperature of the image sensor is higher than a preset threshold.

[0013] Thirdly, this application discloses an electronic device, including: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the aforementioned disclosed image processing method.

[0014] Fourthly, this application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned disclosed image processing method.

[0015] Fifthly, this application discloses a computer program product that, when executed by a processor, implements the aforementioned image processing method.

[0016] As can be seen, this application first obtains the temperature status of the image sensor, and when the temperature status of the image sensor indicates that the temperature of the image sensor is higher than a preset threshold, it enables the thermal noise calibration parameter. Since this thermal noise calibration parameter can accurately reflect the noise characteristics under high temperature conditions, processing the image based on this thermal noise calibration parameter can effectively suppress thermal noise, thereby reducing image jitter noise. In addition, in traditional solutions, adjusting the temporal parameters according to the OB register value requires a multi-frame superposition smoothing process to suppress noise. This process can cause obvious motion blur or ghosting of moving objects on the screen. However, this application can suppress thermal noise by enabling the thermal noise calibration parameter, improving the motion blur problem and making the visual effect of the image better. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0018] Figure 1 This is a flowchart of an image processing method disclosed in this application; Figure 2 This is a flowchart of a specific image processing method disclosed in this application; Figure 3 This is a flowchart of a specific image processing method disclosed in this application; Figure 4 This is a schematic diagram of the structure of an image processing device disclosed in this application; Figure 5 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] For some image sensors, due to their design and manufacturing processes, the dark current inside them drifts as the ambient temperature rises, introducing noise and color cast into the image, i.e., thermal noise. This problem is more pronounced in low-light scenes; the higher the temperature, the more severe the image flickering noise. Traditional noise reduction methods have significant limitations in addressing this issue.

[0021] Therefore, this application proposes an image processing scheme that can effectively reduce image jitter noise and improve motion blur.

[0022] This application discloses an image processing method, see [link to relevant documentation] Figure 1 As shown, the method includes: Step S11: Obtain the temperature status of the image sensor.

[0023] This embodiment can be applied to devices with image sensors or chips or systems connected to image sensors. When the device or system is powered on, the image sensor starts running. At this time, the image sensor is in its initial state and can use ordinary noise calibration parameters by default. At the same time, the chip or system can acquire the temperature status of the image sensor in real time or at regular intervals.

[0024] The temperature state of the image sensor can be determined by obtaining the register value of the target register. For example, when the register value of the target register is higher than a first threshold, the temperature state of the image sensor is determined to be a high-temperature state, that is, the temperature of the image sensor is considered to be higher than a preset threshold. It can be understood that the larger the register value of the target register, the higher the internal temperature of the image sensor or the higher the external ambient temperature.

[0025] The target register and its value indicate the temperature of the image sensor, for example, by indicating the internal temperature of the image sensor through the register value. The target register can be an OB register, an Ndark register at the image sensor end, or something else.

[0026] In this embodiment, the first threshold is obtained through a pre-set process, which includes: selecting multiple different ambient temperatures, such as 25℃, 40℃, 55℃, and 70℃, collecting the register values ​​of the target register corresponding to each ambient temperature, and determining the register value of the target register corresponding to the ambient temperature at which obvious thermal noise begins to appear on the screen as the baseline threshold, for example, the register value of the target register collected at 55℃. The preset threshold corresponds to the aforementioned baseline threshold / first threshold. For example, if the register value of the target register collected at 55℃ is used as the baseline threshold, then the corresponding preset threshold can be 55℃.

[0027] In some embodiments, the benchmark threshold can be directly used as the first threshold.

[0028] In other embodiments, in order to prevent the judgment result from frequently switching due to small fluctuations in ambient temperature around the reference threshold, a preset deviation amount can be added to the reference threshold, and the register value of the target register after adding the preset deviation amount can be set to the first threshold.

[0029] In addition to reading the register value of the target register, it can also directly read the register value inside the image sensor used to characterize the temperature. This register value can directly output the real-time temperature parameter characterizing the image sensor, which can improve the accuracy and reliability of high temperature state determination.

[0030] Understandably, the two temperature reading methods can be used individually or in combination to flexibly adapt to the hardware configurations of different sensor models.

[0031] Step S12: When the temperature status of the image sensor indicates that the temperature of the image sensor is higher than a preset threshold, enable the thermal noise calibration parameter.

[0032] In this embodiment, when the temperature status indicator of the image sensor shows that the temperature of the image sensor is higher than a preset threshold, the thermal noise calibration parameter is activated. The thermal noise calibration parameter is obtained in the following way: (1) First, turn off the image sensor’s Defect Pixel Correction (DPC) function.

[0033] The reason for disabling the dead pixel correction function is that thermal noise under high temperature conditions manifests as random, fluctuating, and patchy noise. If DPC is enabled, thermal noise will be mistakenly treated as dead pixels and masked or corrected, causing the acquired raw image data to lose its true thermal noise characteristics and making it impossible to generate accurate thermal noise calibration parameters.

[0034] (2) Secondly, under high temperature conditions, the original sample image data output by the image sensor is acquired. That is, when the temperature is higher than a preset threshold and the bad pixel correction function of the image sensor is turned off, the original sample image data output by the image sensor is acquired.

[0035] The original sample image data can be raw data. The original sample image data includes the original bright frame image data acquired under the first condition, the original dark frame image data acquired under the second condition, and the original black frame image data acquired under the third condition.

[0036] The original bright frame image data acquired under the first condition is: data acquired when the preset reference color chart reaches the target brightness value under different ISO conditions.

[0037] In some embodiments, the first condition is to collect 30 frames of data at different ISO (International Organization for Standardization) levels on a 24-color chart, and the brightness value of the 19th color block on the 24-color chart is approximately 204, and the maximum ISO value does not exceed the upper limit of the analog gain of the image sensor.

[0038] The original dark frame image data acquired under the second condition is the data acquired after shortening the exposure time by a preset ratio at the aforementioned sensitivity.

[0039] In some embodiments, the second condition is to divide the exposure time corresponding to each ISO level bright frame by 16 and collect 30 frames of data using manual AE (Manual Auto Exposure) mode.

[0040] The original black frame image data collected under the third condition is the data collected when the lens is covered and the exposure time is zero.

[0041] In some embodiments, the third condition is to black out the lens, set the exposure time to 0, and collect 30 frames of data using manual AE mode.

[0042] (3) Finally, noise calibration is performed on the original image data of the sample to obtain thermal noise calibration parameters. This noise calibration process can be generated by importing the original image data of the sample into the calibration tool.

[0043] Since the aforementioned thermal noise calibration parameters can accurately reflect the noise characteristics under high-temperature conditions, image processing based on these calibration parameters can effectively suppress thermal noise and reduce image jitter. Furthermore, in traditional solutions, adjusting temporal parameters based on OB register values ​​requires multi-frame overlay smoothing to suppress noise, which can cause motion blur or ghosting of moving objects on the screen. This application, however, can suppress thermal noise and improve motion blur by enabling the thermal noise calibration parameters, resulting in a better visual effect.

[0044] Furthermore, when performing the step of enabling thermal noise calibration parameters, the following steps are also performed: enabling the bad pixel correction function of the image sensor, wherein the bad pixel correction function is used to detect and repair abnormal pixels on the image sensor. In this way, the image quality defects caused by abnormal pixels on the image sensor can be corrected while reducing image jitter noise and improving motion blur.

[0045] Furthermore, when the temperature state of the image sensor is determined to be non-high temperature, i.e., the temperature state indicates that the temperature of the image sensor is not higher than a preset threshold, the ordinary noise calibration parameter can be enabled. The ordinary noise calibration parameter is different from the thermal noise calibration parameter.

[0046] The general noise calibration parameters are the default noise calibration parameters, which can be obtained by calibrating raw data collected at room temperature. In other words, this embodiment adds an additional set of thermal noise calibration parameters adapted to high-temperature scenarios on the basis of the original general noise calibration parameters, realizing differentiated noise reduction processing for normal and high-temperature scenarios, compatible with more working environments, effectively improving image quality and user experience, and enhancing product applicability and customer stickiness.

[0047] Furthermore, when executing the step of enabling the general noise calibration parameters, the following steps are also performed: Disable the image sensor's dead pixel correction function. This is because, under non-high-temperature conditions, the Image Signal Processing (ISP) module is still performing dead pixel correction normally, the image noise level is low, and there is no obvious jitter noise in the image. At this time, there is no need for the image sensor to assist in dead pixel correction, so the image sensor's dead pixel correction function can be disabled. Disabling the image sensor's dead pixel correction function can avoid the correction algorithm from incorrectly correcting normal pixels, reduce unnecessary image quality loss, and reduce the image sensor's computational overhead.

[0048] In addition, when switching calibration parameters from ordinary noise calibration parameters to thermal noise calibration parameters, or vice versa, dynamic switching can be achieved through the application programming interface.

[0049] For example, when the register value of the target register of the image sensor is greater than the first threshold, it is determined that the temperature of the image sensor is higher than the preset threshold, the image sensor is in a high temperature state, and the normal noise calibration parameters are switched to thermal noise calibration parameters through the application programming interface, and the dead pixel correction function of the image sensor is enabled simultaneously.

[0050] When the register value of the target register is less than the second threshold (or less than the first threshold in some embodiments), it is determined that the temperature of the image sensor is not higher than the preset threshold, and the image sensor is in a non-high temperature state. The thermal noise calibration parameters are switched back to ordinary noise calibration parameters through the application programming interface, and the bad pixel correction function of the image sensor is turned off at the same time. In this way, the calibration parameters and correction functions are automatically adapted and switched according to the temperature state.

[0051] Furthermore, the following describes a method for determining the temperature status by combining at least two thresholds with the register value of the target register.

[0052] Initially, the image sensor uses standard noise calibration parameters by default. When the register value of the target register is higher than the first threshold, the temperature state of the image sensor is determined to be high-temperature. If the temperature of the image sensor is higher than a preset threshold, then thermal noise calibration parameters are enabled.

[0053] As the temperature of the image sensor is continuously monitored, if the register value of the subsequent target register does not exceed the second threshold, it is determined that the temperature of the image sensor is not in a high-temperature state. The image sensor temperature does not exceed a preset threshold; it is understood that the second threshold is less than the first threshold. At this point, the normal noise calibration parameters can be re-enabled.

[0054] The process for determining the second threshold is as follows: Select multiple different ambient temperatures, such as 25℃, 35℃, 45℃ and 50℃, collect the register values ​​of the target register corresponding to each ambient temperature, and determine the register value of the target register corresponding to the ambient temperature where the thermal noise in the picture is significantly reduced to an acceptable level as the second threshold, such as the register value of the target register collected at 45℃.

[0055] By setting a first threshold and a second threshold, the temperature status determination result can be prevented from fluctuating between high-temperature and non-high-temperature states when the OB register temperature fluctuates slightly. For example, the register value of the target register corresponding to the 55℃ operating condition can be set as the first threshold as the criterion for determining whether to enter the high-temperature state, and the register value of the target register corresponding to the 45℃ operating condition can be set as the second threshold as the criterion for determining whether to exit the high-temperature state.

[0056] In actual use, increased daytime light causes the image sensor temperature to gradually rise. Only when the register values ​​of the target registers, such as the OB register, exceed the first threshold is the system officially determined to be in a high-temperature state. When the image sensor temperature is higher than the preset threshold, the system can switch to thermal noise calibration parameters and enable the DPC function. When the ambient light weakens and the image sensor temperature gradually decreases, the system will not immediately exit the processing mode corresponding to the high-temperature state. Instead, it will continue until the OB register value drops below the second threshold before the system determines to be in a non-high-temperature state. When the image sensor temperature drops below the preset threshold, the system can switch to normal noise calibration parameters and disable the DPC function.

[0057] In this way, unnecessary state changes will not occur due to brief, small fluctuations in temperature. It can accurately and promptly activate the thermal noise optimization process at high temperatures to ensure imaging quality in high-temperature environments, and smoothly transition to the normal optimization mode after the temperature gradually returns to normal. In other words, this embodiment is not limited to a single threshold determination method, but can flexibly select either a single threshold determination method or a dual threshold determination method according to the actual application scenario, sensor characteristics, and temperature fluctuations.

[0058] As can be seen, this application first obtains the temperature status of the image sensor, and when the temperature status of the image sensor indicates that the temperature of the image sensor is not higher than a preset threshold, it enables the thermal noise calibration parameter. Since this thermal noise calibration parameter can accurately reflect the noise characteristics under high temperature conditions, processing the image based on this calibration parameter can effectively suppress thermal noise and reduce image jitter noise. In addition, in traditional solutions, adjusting the temporal parameters according to the OB register value requires a multi-frame superposition smoothing process to suppress noise. This process can cause obvious motion blur or ghosting of moving objects on the screen. However, this application can suppress thermal noise and improve the motion blur problem by enabling the thermal noise calibration parameter, resulting in a better visual effect.

[0059] This application discloses a specific image processing method. Compared with the previous embodiment, this embodiment further explains and optimizes the technical solution: considering that thermal noise is more prominent in low-irradiance scenarios, this embodiment limits the temperature state detection and thermal noise suppression process to be triggered only under low-irradiance conditions, and enables ordinary noise calibration parameters under non-low-irradiance conditions. See also Figure 2 As shown, it includes: Step S21: Obtain the raw image data output by the image sensor.

[0060] In this embodiment, the raw image data is the image data directly acquired by the image sensor during the imaging process without image noise reduction and color processing. By acquiring the raw image data output by the image sensor, a data basis is provided for subsequent low irradiance, temperature state detection and calibration parameter switching.

[0061] Step S22: When the original image data is data collected under low irradiance conditions, perform the step: obtain the temperature status of the image sensor.

[0062] In this embodiment, when the digital gain and / or analog gain of the original image data is greater than the corresponding gain threshold, the original image data is data acquired under low irradiance conditions. This is because, under low irradiance conditions, the light signal intensity incident on the image sensor's photosensitive unit is weak. To ensure that the image brightness meets imaging requirements, the image sensor increases the analog gain and / or digital gain to compensate for the light signal intensity. Therefore, by reading the current analog gain and / or digital gain of the image sensor and comparing it with the corresponding gain threshold, it can be determined whether the current condition is low irradiance.

[0063] Analog gain refers to the gain of the analog electrical signal output by the photosensitive unit during the photoelectric conversion stage of the image sensor. It acts at the front end of the signal chain and is mainly used to compensate for the signal strength under low light conditions and improve the signal-to-noise ratio.

[0064] Digital gain refers to the gain applied to the digital image signal after analog-to-digital conversion. It is applied to the back end of the signal chain to further adjust the overall brightness of the image.

[0065] In the first embodiment, when the digital gain of the original image data is greater than the corresponding gain threshold, the original image data is data acquired under low irradiance conditions.

[0066] In the second embodiment, when the simulated gain of the original image data is greater than the corresponding gain threshold, the original image data is data collected under low irradiance conditions.

[0067] In the third embodiment, when both the digital gain and analog gain of the original image data are greater than the corresponding gain threshold, the original image data is data collected under low irradiance conditions.

[0068] In this way, by using different low irradiance criteria, judgments can be made from the perspective of single gain or dual gain combination, avoiding misjudgment or omission due to a single judgment criterion.

[0069] Based on this, when the original image data is acquired under low irradiance conditions, the following step is performed: obtain the temperature status of the image sensor.

[0070] In other words, when the original image data is not collected under low irradiance conditions, there is no need to perform the step of obtaining the temperature status of the image sensor. This reduces unnecessary detection under normal lighting conditions, thereby reducing system computational overhead.

[0071] Step S23: When the temperature status of the image sensor indicates that the temperature of the image sensor is higher than a preset threshold, enable the thermal noise calibration parameter.

[0072] In this embodiment, when the image sensor is in a high-temperature state, the thermal noise calibration parameter is enabled. The details of step S23 are as described in the previously disclosed embodiments and will not be repeated here.

[0073] To better illustrate the solutions in the above embodiments, the following is combined with... Figure 3 Provide an example. Figure 3 This is a flowchart of a specific image processing method provided in this embodiment.

[0074] First, begin imaging to determine if the area is under low irradiance conditions. If not, proceed directly with processing using standard noise calibration parameters.

[0075] If so, the register value of the target register of the image sensor is further read and it is determined whether it is greater than the first threshold. If the register value of the target register is not greater than the first threshold, the normal noise calibration parameters are used and the DPC function is turned off. If the register value of the target register is greater than the first threshold, the thermal noise calibration parameters are used and the DPC function is turned on.

[0076] Both of the above processing methods continuously monitor the register value of the target register to achieve dynamic switching of calibration parameters. This effectively suppresses thermal noise, improves motion blur, and corrects bad pixel defects in low irradiance and high temperature scenarios. At the same time, it reduces computational overhead in non-low irradiance or normal temperature scenarios, achieving differentiated noise reduction processing under different operating conditions.

[0077] Taking outdoor monitoring scenarios in the summer afternoon as an example, for Figure 3 The image processing flow is explained as follows: After the monitoring equipment starts imaging, it first determines whether the current irradiance condition is low. Although it is a strong afternoon light environment, the lighting in the shaded areas of trees and backlit areas of buildings in the monitoring image is insufficient. The system determines that it is a low irradiance condition and then reads the register value of the target register of the image sensor (this value increases with the temperature, indirectly reflecting the sensor temperature status). Since the monitoring equipment has been exposed to the sun for a long time, the register value of the target register exceeds the first threshold. Based on this, it is determined that the temperature of the image sensor is higher than the preset threshold, and the image sensor is in a high temperature state.

[0078] according to Figure 3 As shown in the process, the system enables thermal noise calibration parameters and activates the DPC dead pixel correction function, effectively suppressing thermal noise caused by high temperature, reducing image jitter noise, improving motion blur, and correcting dead pixel defects. During imaging, the system continuously monitors the register value of the target register to achieve dynamic switching of calibration parameters: if the register value of the target register falls below the first threshold, the system uses ordinary noise calibration parameters and disables the DPC function.

[0079] Both of the above processing methods continuously monitor the register value of the target register, achieving thermal noise suppression, ghosting improvement and bad pixel correction in low irradiance and high temperature scenarios, and reducing computational overhead in non-low irradiance or normal temperature scenarios, thereby completing differentiated noise reduction processing.

[0080] As can be seen, this application proposes an intelligent image thermal noise optimization mechanism that can effectively improve jitter noise in the image and reduce motion blur in moving images. This method does not rely on multi-frame fusion for noise reduction; when the image is static, it can effectively filter out excess noise, ensuring a clean and clear image; when moving targets appear in the image, it will not affect the object's imaging shape, resulting in a better visual effect. Furthermore, this application is compatible with existing imaging processing logic and can adapt to various practical application conditions.

[0081] Therefore, this application first acquires the raw image data output by the image sensor. When the raw image data is acquired under low irradiance conditions, the temperature status of the image sensor is obtained. Then, when the temperature status of the image sensor indicates that the temperature of the image sensor is greater than a preset threshold, indicating that the image sensor is in a high-temperature state, the thermal noise calibration parameter is activated. Since this application performs the step of acquiring the temperature status of the image sensor when the raw image data is acquired under low irradiance conditions, and thermal noise is more prominent in low irradiance scenarios, this application can reduce unnecessary detection and processing under normal lighting conditions, thereby reducing system computational overhead. Furthermore, since the thermal noise calibration parameter can accurately reflect the noise characteristics under high-temperature conditions, processing the image based on the thermal noise calibration parameter can effectively suppress thermal noise, thereby reducing image flicker noise.

[0082] Furthermore, in traditional solutions, adjusting temporal parameters based on OB register values ​​requires multi-frame overlay smoothing to suppress noise. This process can cause motion objects to produce noticeable ghosting or afterimages on the screen. However, this application can suppress thermal noise by enabling thermal noise calibration parameters, thus improving the ghosting problem of motion and resulting in better visual effects.

[0083] Accordingly, this application also discloses an image processing apparatus, see [link to relevant documentation]. Figure 4 The device includes: Temperature status acquisition module 11 is used to acquire the temperature status of the image sensor; The calibration parameter enabling module 12 is used to enable thermal noise calibration parameters when the temperature state of the image sensor indicates that the temperature of the image sensor is higher than a preset threshold.

[0084] For a more detailed description of the working process of the temperature status acquisition module 11 and the calibration parameter activation module 12, please refer to the relevant content disclosed in the foregoing embodiments, which will not be repeated here.

[0085] Furthermore, embodiments of this application also provide an electronic device. Figure 5 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0086] Figure 5This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a display screen 23, an input / output interface 24, a communication interface 25, a power supply 26, and a communication bus 27. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the image processing method disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0087] In this embodiment, the power supply 26 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 25 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 24 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0088] Furthermore, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk, or optical disk, etc. The resources stored thereon may include computer programs 221, and the storage method may be temporary storage or permanent storage. The computer programs 221 may include, in addition to computer programs capable of performing the image processing methods executed by the electronic device 20 as disclosed in any of the foregoing embodiments, computer programs capable of performing other specific tasks.

[0089] Furthermore, embodiments of this application also disclose a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned disclosed image processing method.

[0090] For the specific steps of this method, please refer to the relevant content disclosed in the foregoing embodiments, which will not be repeated here.

[0091] The various embodiments in this application are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. For the same or similar parts between the various embodiments, refer to each other. As for the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and relevant parts can be referred to in the method section.

[0092] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0093] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0094] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0095] The above provides a detailed description of an image processing method, apparatus, device, and storage medium provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An image processing method, characterized by, include: Acquire the temperature status of the image sensor; When the temperature status of the image sensor indicates that the temperature of the image sensor is higher than a preset threshold, the thermal noise calibration parameter is enabled.

2. The image processing method of claim 1, wherein, When performing the step of enabling thermal noise calibration parameters, the following steps are also performed: Enable the dead pixel correction function of the image sensor.

3. The image processing method of claim 1 or 2, characterized in that, After acquiring the temperature status of the image sensor, the method further includes: When the temperature status indicator of the image sensor indicates that the temperature of the image sensor is not higher than a preset threshold, the general noise calibration parameter is enabled. The general noise calibration parameter is different from the thermal noise calibration parameter. When performing the step of enabling common noise calibration parameters, the following steps are also performed: Disable the dead pixel correction function of the image sensor.

4. The image processing method of claim 1 or 2, characterized by, The acquisition of the temperature status of the image sensor includes: Obtain the register value of a target register in the image sensor, the target register being used to indicate the temperature of the image sensor; When the register value of the target register is higher than a first threshold, the temperature status of the image sensor indicates that the temperature of the image sensor is higher than a preset threshold.

5. The image processing method of claim 1, wherein, The calibration process for the thermal noise calibration parameters includes: When the temperature is higher than a preset threshold and the bad pixel correction function of the image sensor is turned off, the original sample image data output by the image sensor is acquired. Noise calibration was performed on the original image data of the samples to obtain thermal noise calibration parameters.

6. The image processing method of claim 1 or 2, characterized by, Before acquiring the temperature status of the image sensor, the method further includes: Acquire the raw image data output by the image sensor; When the original image data is acquired under low irradiance conditions, the following step is performed: obtain the temperature status of the image sensor.

7. The image processing method of claim 6, wherein, When the digital gain and / or analog gain of the original image data is greater than the corresponding gain threshold, the original image data is data acquired under low irradiance conditions.

8. An image processing apparatus characterized by comprising: include: Temperature status acquisition module, used to acquire the temperature status of the image sensor; The calibration parameter enabling module is used to enable thermal noise calibration parameters when the temperature status indicator of the image sensor indicates that the temperature of the image sensor is higher than a preset threshold.

9. An electronic device, comprising: include: Memory, used to store computer programs; A processor for executing the computer program to implement the image processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program; wherein, when the computer program is executed by a processor, it implements the image processing method as described in any one of claims 1 to 7.

Images