15 results about "Low dynamic range" patented technology

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.

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.

This disclosure provides a method, device, system, and medium for taking pictures with a head-mounted device, relating to the field of head-mounted device technology. The method, when applied to a head-mounted device, includes: acquiring a first set of original images upon receiving a picture-taking command; determining the current environmental scene category based on the first set of original images, including high dynamic range (HDR) and low dynamic range (LVR) categories; determining the original image acquisition method and a first image preprocessing method corresponding to the current environmental scene category; processing a second set of original images acquired using the original image acquisition method according to the first image preprocessing method to obtain a first preprocessed original image; and sending the first preprocessed original image to a control device communicatively connected to the head-mounted device, whereby the control device, upon receiving the first preprocessed original image, generates a picture based on it. This method reduces the overall power consumption of the head-mounted device.

In an embodiment, a method includes obtaining a high dynamic range (HDR) image and generating low dynamic range (LDR) images based on the HDR image, wherein at least some of the LDR images are associated with different exposure levels. The method further includes generating tone-type weight maps based on the LDR images, wherein at least one of the LDR images is associated with two or more of the tone-type weight maps. The method further includes generating blending weights for the LDR images based on the tone-type weight maps, wherein the blending weights for at least one of the LDR images are based on at least two of the tone-type weight maps associated with at least two of the LDR images.

The present application relates to display control technical field, specifically to a kind of LCD display brightness control method and system, method is applied to the display device containing direct backlight module and liquid crystal panel, backlight module is the multi-partition light source of independently adjustable brightness, method includes: constructing backlight module diffusion simulation module, based on point spread function to backlight brightness distribution diffusion obtains backlight output image;Utilize training sample to train HDR reconstruction model and local dimming model, HDR reconstruction model is based on saturation discrimination generation feature mask and is propagated in convolution feature extraction to inhibit saturated area feature, local dimming model generates supervision signal by means of diffusion simulation module and is updated with the loss function containing image fidelity term and power consumption constraint term;Display stage carries out luminance correction to input low dynamic range image and combines feature mask and exports target HDR image;Again output backlight prediction image and obtain backlight output image by diffusion simulation;According to the partition topological partition convergence forms backlight control image, and by target HDR image and backlight output image generates liquid crystal panel control image representation pixel transmittance;Accordingly drive each partition luminous intensity and each pixel transmittance, realize backlight and liquid crystal collaborative modulation output display image.The present application generates backlight partition control and liquid crystal panel transmittance control, so that display output approximates target HDR image and gives consideration to power consumption constraint and halo artifact suppression.

Embodiments of the present application provide a high dynamic range image processing method, a model training method, an apparatus, a device, a medium and a product. The method comprises: obtaining source image data to be processed, inputting the source image data into an image processing model, processing the source image data by the image processing model, obtaining an HDR image corresponding to the source image data and LED brightness data according to the processing of the image processing model, determining liquid crystal transmittance of a display device according to the HDR image, determining backlight brightness of the display device according to the LED brightness data, and determining HDR display content of the HDR image in the display device according to the liquid crystal transmittance and the backlight brightness. The method is used to realize effective conversion of an image with a lower dynamic range to a high dynamic range image, and improve the display quality of the high dynamic range image.

In some examples, a computing system accesses a field of view (FOV) image that has a field of view less than 360 degrees and has low dynamic range (LDR) values. The computing system estimates lighting parameters from a scene depicted in the FOV image and generates a lighting image based on the lighting parameters. The computing system further generates lighting features generated the lighting image and image features generated from the FOV image. These features are aggregated into aggregated features and a machine learning model is applied to the image features and the aggregated features to generate a panorama image having high dynamic range (HDR) values.

A system and method are disclosed to address the need for adaptive exposure in high dynamic range (HDR) images. The solution can leverage recent advances in the use of virtual reality (VR) headsets and augmented reality (AR) displays equipped with infrared (IR) eye-tracking devices. The gaze vector determined by the eye-tracking device identifies one or more fixation points on the image corresponding to areas where underexposure exists. Exposure around the fixation points can be adaptively corrected using image processing techniques. Using spatially adaptive exposure, the resulting image, a kind of foveal image, can be rendered with sufficient detail on a low dynamic range (LDR) display.

This application discloses an image processing method, apparatus, and electronic device, belonging to the field of artificial intelligence technology. The method includes: acquiring at least two mask images and an image restoration guide map corresponding to at least two first images, wherein the mask images are used to indicate the area to be restored in the first images, and the image restoration guide map is used to indicate the restoration target of the area to be restored, and the first images are low dynamic range (LDR) images; and performing image restoration processing on at least two first images based on the at least two mask images and the image restoration guide map to obtain high dynamic range (HDR) images.

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 application discloses an unsupervised optical flow estimation method and system for different exposure low dynamic range images, and relates to the technical field of optical flow estimation. First, based on an intensity mapping function (IMF), the brightness of low dynamic range images with different exposures is normalized. Then, based on a RAFT algorithm, the low dynamic range images after brightness normalization are subjected to preliminary optical flow estimation. Based on the result of the preliminary optical flow estimation, the RAFT algorithm is trained by using an unsupervised learning method. Finally, the low dynamic range images after brightness normalization are subjected to final optical flow estimation by using the trained RAFT algorithm. The application can be applied to images with different exposures and images under more complex lighting conditions, and the best optical flow estimation result can be achieved, which is more efficient and more robust than existing methods.

The application particularly relates to a high dynamic range imaging method based on space-frequency interaction, which comprises the following steps: taking a low dynamic range image as input, generating a corresponding high dynamic range image through gamma correction, processing the high dynamic range image through a shared convolution layer after splicing, and generating initial alignment features by using an attention-based alignment method; respectively performing space domain and frequency domain feature extraction on the initial alignment features; adaptively balancing the contributions of the space domain and frequency domain features through a cross-domain feature fusion block to generate fusion features; extracting key structure clues from the fusion features by using a prompt optimization module, and directionally repairing degradation areas such as oversaturation and misplacement; and finally outputting a high-fidelity high dynamic range image. The application fully gives play to the complementary advantages of space-frequency dual domains, and combines an efficient attention mechanism and a learnable prompt template, so that the image structure consistency and detail integrity can still be guaranteed in an extreme light and large motion scene.

A forward reshaping mapping is generated to map a source image to a corresponding forward reshaped image of a lower dynamic range. The source image is spatially downsampled to generate an image, noise is injected into the resized image to generate an injected noise image. The forward reshaping mapping is applied to map the injected noise image to generate the lower dynamic range embedded noise image. The video signal is encoded with the embedded noise image and transmitted to a recipient device for the recipient device to render a display image generated from the embedded noise image.

The application belongs to the technical field of unsupervised high dynamic range imaging. The application provides an unsupervised high dynamic range imaging method based on a diffusion model. According to the brightness consistency and structure constraint between multi-exposure low dynamic range images, the disclosure embodiment guides a high dynamic range reconstruction network to learn, so as to obtain a preliminary high dynamic range reconstruction result with reasonable brightness and reliable structure. Then, based on exposure information and reconstruction difference, static regions and motion regions are distinguished, the static regions are enhanced in detail, and the motion or unreliable information regions are accurately marked. Finally, a diffusion model is introduced, the unobservable regions caused by motion, occlusion or exposure problems are completed with high-quality content on the premise of keeping the known static regions from being damaged, so that the structure complete reconstruction and visual quality improvement of the high dynamic range image in the dynamic scene are realized.