103 results about "Low dynamic range" patented technology

Implementations relate to providing an HDR image format with low dynamic range compatibility. In some implementations, a computer-implemented method includes obtaining a first image depicting a scene and having a first dynamic range, obtaining a second image depicting the scene and having a second dynamic range that is different than the first dynamic range, and generating a recovery map based on the first and second images. The recovery map encodes differences in luminances between corresponding portions of the first and second images, the differences scaled by a range scaling factor including a ratio of maximum luminances of the second image and the first image. The first image and recovery map are provided in an image container that is readable to display a derived image that is based on applying the recovery map to the first image and has a dynamic range that is different than the first dynamic range.

Embodiments relate to providing an HDR video format with low dynamic range compatibility. In some embodiments, a method includes acquiring a first video including first frames having a first dynamic range and a second video including corresponding second frames having a second dynamic range different from the first dynamic range. A recovery map track is generated including a recovery map frame for each first frame and each corresponding second frame, where the recovery map frame encodes a luminance difference between a portion of the first frame and a portion of the corresponding second frame. The first video and the recovery map track are provided in a video container that is readable to display the first video or to display a derived video based on applying the recovery map track to the first video. The derived video includes derived frames having a dynamic range different from the first dynamic range.

The invention discloses a carrier tracking method suitable for a multi-bit spread spectrum signal, and relates to the technical field of multi-bit spread spectrum, the carrier tracking method comprises the steps of coarse frequency offset correction, multi-bit spread spectrum, average phase estimation and delay phase correction.The carrier tracking method comprises the steps that firstly, a sliding DFT algorithm is adopted to estimate frequency offset, and a coarse frequency offset available correction signal is obtained; secondly, adaptively selecting the number of available windows according to historical data corresponding to the current signal-to-noise ratio; the method comprises the following steps: firstly obtaining a window intermediate point spread spectrum period, then extracting a carrier phase estimation value of the window intermediate point spread spectrum period, finally delaying a correlation value by the spread spectrum period to realize time sequence alignment and eliminate phase deviation, and finally obtaining information bits corresponding to each spread spectrum period by extracting and correcting a real part of the correlation value, matching a local sequence number and mapping. According to the method, the phase estimation precision under the low signal-to-noise ratio can be improved, parameter self-adaption to high and low dynamic scenes of a satellite is realized, the coherent judgment performance is improved, and the hardware complexity is reduced.

The invention discloses a high dynamic range imaging method for an ultrahigh-definition video, and the method comprises the following steps: collecting a low dynamic range frame of the ultrahigh-definition video, splicing images through exposure normalization and adaptive correction, and constructing multi-scale multi-channel data; extracting structure and texture features by using an improved multi-scale convolution and attention network, and calculating matching degree rearrangement support frame features; detecting a missing region in the image, and performing local recovery and sharpening on the missing region by using improved gating convolution in combination with residual errors and high-frequency enhancement branches; dynamically calculating a fusion weight, carrying out weighted fusion on the features, and outputting a final fusion feature; and inputting the fusion features into a global merging network for HDR reconstruction, and splicing the fusion features into a video sequence for coding and outputting by adopting adaptive mapping and time domain processing. The method has high robustness and noise immunity, video frames in a complex illumination environment can be effectively processed, HDR images with rich details and accurate colors are generated, and the visual effect of the video is remarkably improved.

Disclosed is a method for enabling exposure-guided three-dimensional (3D) reconstruction, including (i) receiving Low Dynamic Range (LDR) input image captured by camera; (ii) rendering High Dynamic Range (HDR) image corresponding to input image, using HDR 3D reconstruction model pre-trained for HDR image reconstruction of 3D environment; (iii) applying HDR to LDR tone-mapping operator on HDR image, for producing tone-mapped LDR image; (iv) determining first loss function (FLF) between input image and tone-mapped LDR image, FLF comprising weighted pixel value differences between pixels of input image and corresponding pixels of tone-mapped LDR image; (v) determining whether input image has saturated pixel(s), saturated pixel(s) comprises: highlight saturated pixel, shadow saturated pixel; (vi) de-weighting pixel value difference corresponding to saturated pixel in FLF; (vii) back-propagating gradient of FLF with respect to model parameters, through differentiable render function of HDR 3D reconstruction model, for adjusting model parameters in way that reduces FLF.

The invention belongs to the related technical field of image processing, and discloses an illumination estimation method and system based on image semantic driving and a system training method.The method comprises the steps that an original small-view-angle image in a target environment is preprocessed to obtain a low-dynamic-range illumination image, then multi-level feature extraction is conducted on the low-dynamic-range illumination image, and the low-dynamic-range illumination image is obtained; the method comprises the following steps of: obtaining a shallow-layer feature map, a middle-layer feature map and a deep-layer feature map, performing feature enhancement processing on the middle-layer feature map, performing weighting processing on the shallow-layer feature map and the deep-layer feature map based on an attention mechanism, fusing the feature maps of the three levels, and performing linear regression operation to obtain a prediction result of illumination parameters. On the basis of the process, the light source false detection rate can be greatly reduced, the cross-scene generalization ability of the model can be improved, explicit enhancement of a real light source and effective suppression of an error light source are achieved, the prediction result is more accurate, and on the basis of the design, the accuracy of illumination estimation can be improved under limited training samples.

Implementations relate to providing HDR video formats with low dynamic range compatibility. In some implementations, a method includes obtaining a first video including first frames having a first dynamic range and a second video including corresponding second frames having a second dynamic range that is different than the first dynamic range. A recovery map track is generated, including a recovery map frame for each first frame and corresponding second frame, the recovery map frame encoding differences in luminances between portions of the first frame and corresponding second frame. The first video and recovery map track are provided in a video container that is readable to display the first video or to display a derived video based on applying the recovery map track to the first video. The derived video includes derived frames that have a dynamic range different than the first dynamic range.

In some examples, the method for tone mapping a high dynamic range (HDR) image to a lower dynamic range image (TDR) comprises obtaining a luminous intensity image based on the HDR image, processing the luminous intensity image using a feature extractor network, whereby to generate a feature map, processing the feature map using at least one neural network module of multiple neural network modules, obtaining a tone mapping operator based on an output of the at least one neural network module of the multiple neural network modules, and applying the tone mapping operator to the HDR image, whereby to obtain the TDR image, wherein the tone mapping operator comprises a tone mapping curve.

The application relates to a high-dynamic-range seismic imaging profile gain method, device, medium and equipment, wherein the method comprises the following steps: normalizing an original seismic profile according to seismic profile data to form a normalized profile and control the fluctuation range of the data; performing nonlinear color scale mapping on the normalized profile to map high-dynamic-range seismic profile data to a low-dynamic-range; calculating the resolution of the seismic profile data mapped to the low-dynamic-range at different scales to obtain the reference brightness of each point; and performing light-shading-light-increasing mapping on the basis of the reference brightness of each point to increase local details and obtain a final display profile. The application can significantly enhance weak signals while retaining the relative size relationship between signals and the detail information of different scales, and realizes adaptive gain of a high-dynamic-range seismic profile.

An image processing apparatus comprises a receiver (201) for receiving an image signal which comprises at least an encoded image and a target display reference. The target display reference is indicative of a dynamic range of a target display for which the encoded image is encoded. A dynamic range processor (203) generates an output image by applying a dynamic range transform to the encoded image in response to the target display reference. An output (205) then outputs an output image signal comprising the output image, e.g. to a suitable display. The dynamic range transform may furthermore be performed in response to a display dynamic range indication received from a display. The invention may be used to generate an improved High Dynamic Range (HIDR) image from e.g. a Low Dynamic Range (LDR) image, or vice versa.

Global index values for selecting global reshaping functions for an input image are generated using luminance codewords in the input image that have a relatively low dynamic range. Image filtering is applied to the input image to generate a filtered image. Filtered values of the filtered image provide a measure of local brightness levels in the input image. Local index values for selecting particular local reshaping functions for the input image are generated using the global index values and the filtered values of the filtered image. A reshaped image of a relatively high dynamic range is generated by reshaping the input image with the particular local reshaping functions selected using the local index values.

A method for training an image processing model includes: inputting a low dynamic range image into a first initial image processing model, so that a high dynamic range reconstruction processing is performed on the low dynamic range image to generate a first high dynamic range image; inputting the low dynamic range image into a second initial image processing model to generate a first coefficient; generating a second high dynamic range image according to the first high dynamic range image and the first coefficient; generating a loss function according to data pairs of the second high dynamic range image and a real high dynamic range image, the real high dynamic range image corresponding to the low dynamic range image; and training the first initial image processing model and the second initial image processing model with the loss function.

The application provides an image processing method and device, a computer readable storage medium and an electronic device, relates to the technical field of image processing, and can cut a first image corresponding to a first color depth into a second image corresponding to a second color depth, and normalize the second image into a sample image of the first color depth to simulate a low dynamic range image. Application of such a sample image in model training can facilitate the model to learn the problem of the low dynamic range image, thereby avoiding the model from generating an image with a highlight edge smear problem.

The present invention relates to the field of dynamic vision sensors, and relates in particular to a photosensitive element array for detecting time-related image data. The photosensitive element array is composed of R rows × C columns of units. Each unit comprises: a light sensor having a dynamic range compression characteristic, the light sensor autonomously converting, without any external control, a high-dynamic-range incident light signal into a low-dynamic-range output voltage signal; a plurality of voltage sampling storage units, the stored voltages being obtained by sampling the output voltage signal of the light sensor at different time points; and a voltage readout circuit, configured to read out, simultaneously or in a time-division manner, the voltages in the voltage sampling storage units to the periphery of the photosensitive element array. The present invention further relates to a peripheral readout circuit for the photosensitive element array. The peripheral readout circuit comprises a row scanning and timing control module, C amplifiers, and C analog-to-digital converters capable of sensing field-of-view activity, wherein each analog-to-digital converter adjusts the number of effective conversion cycles in each conversion on the basis of the amplitude of a visual signal sensed by units in a column corresponding to the analog-to-digital converter.

The invention relates to a phase adjusting circuit and method and a distance measuring device. The phase adjustment circuit comprises: at least one adjustment unit configured to adjust an input signal according to an initial gain coefficient to generate an adjustment signal; and changing a gain coefficient for adjusting the input signal in response to a gate control signal; wherein when the adjusting unit has different gain coefficients, the cut-off frequencies corresponding to the gain coefficients are the same; and the control unit is coupled with the adjusting unit and is configured to generate the gating control signal to the adjusting unit according to the adjusting signal and a preset signal. By adopting the scheme, the phase delay in the dynamic range can be reduced, and the distance measurement precision is improved.

The invention discloses a video frame insertion method based on event information, and the method comprises the steps: carrying out the linearization of an original color image in a low dynamic range, and mapping the linearized image to a logarithm domain; carrying out eigendecomposition on the linearized image, and obtaining a corresponding reflection image and an intensity image in a logarithm domain; updating the original intensity graph through the event information to obtain a logarithmic intensity graph of the target frame; synthesizing a logarithmic reflection image of the target frame through the new intensity image and the original reflection image; and finally, recovering the reflection image and the intensity image of the logarithm domain into a linear image. According to the method, the original video frame rate can be increased by any multiple times as required, a high-dynamic-range result is obtained, and the overall image effect is improved. Meanwhile, the problem of dependence on the quality and scale of training data in a traditional frame interpolation algorithm is solved, and the influence of space consistency in color images and event information and the shooting frame rate of the training data on a frame interpolation result is reduced.

The model training method, the illumination determination method, the device and the program product provided by the present disclosure relate to the technical field of image processing, and include the following steps: acquiring low dynamic range images of a preset environment acquired based on different exposure values and actual illumination values of the preset environment when the low dynamic range images are acquired; and training a preset model according to each low dynamic range image and the actual illumination value corresponding to the low dynamic range image to obtain an illumination prediction model. In the model training method, the illumination determination method, the device and the program product provided by the present disclosure, a predicted illumination value can be obtained according to the acquired low dynamic range image in a store, data does not need to be collected by a dedicated person, and the brightness of a lighting lamp in the store can be adjusted in real time according to the predicted illumination value.

The invention discloses a simulation-based high dynamic range image synthesis method, which comprises the following steps of: generating low dynamic range images with different exposure degrees based on a CARLA simulator, and synthesizing a plurality of low dynamic range images into a high dynamic range image by using a multi-exposure fusion algorithm, thereby constructing a large-scale automatic driving high dynamic range simulation data set. According to the invention, through a multi-exposure fusion algorithm, low-dynamic-range images of multiple exposures are concisely and efficiently fused, a high-quality high-dynamic-range image which retains details of a dark part and a bright part at the same time is obtained, and the synthesis speed is relatively high. The method supports the synthesis of a large-scale automatic driving high-dynamic range policy data set, and is used for the research of related tasks.

A high dynamic range (HDR) depth buffer is received at a remote computer. The HDR depth buffer is formed into a plurality of tiles. For each tile, a respective maximum and minimum value of the HDR depth buffer in a region greater than a width of the respective tile is determined. An initial pair of piecewise bilinear bounding functions for the HDR depth buffer is determined using the determined maximum and minimum depth values. For each tile, the initial pair of piecewise bilinear bounding functions is iteratively adjusted to move the respective minimum and maximum depth value of each tile closer to the HDR depth buffer, wherein no adjacent tile is adjusted in the same iteration. Using the adjusted pair of piecewise bilinear bounding functions, a low dynamic range (LDR) depth buffer and tile data are generated and encoded using a video encoder and a lossless compressor respectively.

The invention discloses an array and difference beam co-excitation optimization method and system based on unit dynamic range ratio constraint and a storage medium, and mainly solves the problem that an array and difference beam directional diagram cannot be optimized in a complex application scene and the dynamic range ratio cannot be reduced at the same time in the prior art. The implementation scheme comprises the following steps: determining an antenna optimization index; optimizing the planar array antenna and the difference beam directional diagram by adopting a convex optimization algorithm, and solving the planar array antenna and the difference beam directional diagram to obtain an initial sum beam complex excitation coefficient meeting a constraint condition; adding a unit dynamic range ratio constraint in a planar array antenna and difference beam directional diagram optimization problem, and converting the directional diagram optimization problem after adding the unit dynamic range ratio constraint into a series of convex optimization sub-problems; iteratively solving the convex optimization sub-problem by using a solver to obtain an optimal sum beam excitation coefficient; and synthesizing a sum and difference beam directional diagram of the array according to the optimal sum beam excitation coefficient. According to the invention, the sum-difference beam synthesis with a small dynamic range ratio can be effectively realized while the radiation performance constraint is satisfied, and the method can be used for a monopulse radar system with a cover.

Generating a High Dynamic Range (HDR) panoramic image, comprising: accessing a field of view (FoV) image 302 that has a field of view less than 360 degrees and has Low Dynamic Range (LDR) values; gene

Embodiments of the present application relate to the technical field of image processing, and disclose an image processing method, a chip, an electronic device and a computer readable storage medium. The image processing method comprises: acquiring a tone mapping curve corresponding to a high dynamic range (HDR) image to be processed; selecting a control point in a coordinate system in which the tone mapping curve is located according to a dynamic range of the HDR image; determining a first ordinate corresponding to the abscissa of the control point on the tone mapping curve; if the first ordinate is not in a preset range, adjusting the tone mapping curve so that a second ordinate corresponding to the abscissa of the control point on the adjusted tone mapping curve is in the preset range; and obtaining a low dynamic range (LDR) image corresponding to the HDR image according to the adjusted tone mapping curve, so that the distortion of the LDR image obtained after compression can be avoided while the HDR image is compressed into the LDR image, and thus the LDR image obtained after compression meets the perception of the human eye.

The present invention discloses a method for indoor scene illumination estimation based on a local-to-global completion strategy. This method aims to estimate the illumination conditions at any position in a single restricted-view indoor scene image and decomposes the indoor scene illumination estimation problem into three subtasks: depth-based image warping, low dynamic range (LDR) panorama completion, and high dynamic range (HDR) reconstruction. Based on the second subtask, this method proposes a local-to-global panorama completion strategy. This strategy first performs depth-guided local completion on the sparse panorama output after the first stage of warping to fill small and dense holes. Then, a reasonable global completion is performed using a cubemap projection method and a transformer network (called PanoTransformer) capable of fitting long-range dependencies. Finally, a physically reasonable panorama with texture details can be restored at any position in the input image to capture the spatially varying indoor illumination.

This invention discloses an optical power measurement method, control device, and optical power measurement equipment, relating to the field of optical power measurement technology. The optical power measurement equipment includes multiple signal conditioning circuits, each including a photoelectric conversion circuit, an optical power detection circuit, and an analog-to-digital conversion circuit. The method generates and sends hard-timing pulses in response to a trigger signal, controlling each channel to sequentially complete photoelectric conversion, voltage conversion, and analog-to-digital conversion, and filtering the digital signal. Subsequently, the operating temperature, temperature compensation coefficient, and calibration coefficient containing multiple sub-coefficients are acquired, and temperature compensation is performed on the signal. Then, based on the power range of the signal, the corresponding sub-calibration coefficients are matched, and the optical power measurement signal is calculated through linear fitting and transmitted to the host computer. Thus, the technical solution of this invention can solve the problems of low accuracy, narrow dynamic range, and large temperature drift error in existing multi-channel power meter measurement equipment for synchronous optical power measurement.

A high dynamic range (HDR) depth buffer is received at a remote computer. The HDR depth buffer is formed into a plurality of tiles. For each tile, a respective maximum and minimum value of the HDR depth buffer in a region greater than a width of the respective tile is determined. An initial pair of piecewise bilinear bounding functions for the HDR depth buffer is determined using the determined maximum and minimum depth values. For each tile, the initial pair of piecewise bilinear bounding functions is iteratively adjusted to move the respective minimum and maximum depth value of each tile closer to the HDR depth buffer, wherein no adjacent tile is adjusted in the same iteration. Using the adjusted pair of piecewise bilinear bounding functions, a low dynamic range (LDR) depth buffer and tile data are generated and encoded using a video encoder and a lossless compressor respectively.

Various examples are provided related to high dynamic range (HDR) imaging of a retina. In one example, a fundus camera includes an imaging sensor; an ophthalmic lens; and a source of input light. The source can illuminate the retina at sequentially increasing illumination power levels during a series of illumination periods within a pupillary reflex time of the eye. The imaging sensor can capture a corresponding low dynamic range (LDR) image of during each of the series of illumination periods, which can be combined to generate a HDR image of the retina. An external fixation target can be used to capture LDR images at different visual angles to generate HDR images, which can be combined to generate widefield HDR images.

Systems and methods are disclosed that address the need for adaptive exposure within high dynamic range (HDR) images. Solutions can leverage recent advances in the use of virtual reality (VR) headsets and Augmented Reality (AR) displays equipped with infrared (IR) eye tracking devices. A gaze vector determined by the eye tracking device identifies one or more fixation points on the image that corresponds to an area where there exists a faulty exposure. The exposure around the fixation point can be adaptively corrected using image processing techniques. Using spatial adaptive exposure, the resulting image, a type of foveated image, can be rendered on a low dynamic range (LDR) display with sufficient detail.

This application provides a method, system, electronic device, and medium for high dynamic range (HMR) reconstruction based on a single image. The method includes: processing a high RMR image used for training into a low RMR image to be processed and an optimized low RMR image; using the low RMR image to be processed as input to a partially convolutional U-shaped network in a two-stage deep learning model to train the partially convolutional U-shaped network, thereby obtaining an intermediate low RMR image; using the intermediate low RMR image to train a classical convolutional U-shaped network in the two-stage deep learning model, thereby obtaining a reconstructed HMR image; and inputting a single low RMR image into the trained two-stage deep learning model to obtain the reconstructed HMR image. This application enables HMR reconstruction based on a single image through a two-stage deep learning model, improving the reconstruction quality of HMR images based on a single image.

This invention relates to the field of display technology, specifically to a display control method and system for a computer LCD monitor. The method includes: acquiring a low dynamic range image, normalizing its brightness, and generating a mask indicating exposure / saturation areas based on a saturation threshold; inputting the image and mask into a high dynamic range reconstruction network to obtain a reconstructed high dynamic range image; combining its color and brightness channels into a tensor input local dimming network to output a backlight prediction image consistent with the display resolution; simulating a diffuser plate based on point spread function convolution and calculating the average brightness of sub-regions according to the LED array to generate a backlight control image; calculating and cropping the transmittance from the reconstructed image and the diffused backlight image to form a grayscale control image for the LCD panel; simultaneously sending the backlight control image and the grayscale control image to drive the backlight module and the LCD panel to achieve high dynamic range display. This invention solves problems in existing computer LCD monitors, such as the difficulty in fully utilizing the backlight local dimming potential of low dynamic range input images, the difficulty in simultaneously achieving bright area saturation and dark area detail, and the high backlight power consumption and susceptibility to halo artifacts while improving contrast.

An image processing apparatus comprises a receiver (201) for receiving an image signal which comprises at least an encoded image and a target display reference. The target display reference is indicative of a dynamic range of a target display for which the encoded image is encoded. A dynamic range processor (203) generates an output image by applying a dynamic range transform to the encoded image in response to the target display reference. An output (205) then outputs an output image signal comprising the output image, e.g. to a suitable display. The dynamic range transform may furthermore be performed in response to a display dynamic range indication received from a display. The invention may be used to generate an improved High Dynamic Range (HDR) image from e.g. a Low Dynamic Range (LDR) image, or vice versa.