NEURAL NETWORKS FOR DYNAMIC RANGE CONVERSION AND DISPLAY MANAGEMENT OF IMAGES

    公开(公告)号:US20250095125A1

    公开(公告)日:2025-03-20

    申请号:US18291578

    申请日:2022-07-22

    Abstract: Methods and systems for dynamic range conversion and display mapping of standard dynamic range (SDR) images onto high dynamic range (HDR) displays are described. Given an SDR input image, a processor generates an intensity (luminance) image and optionally a base layer image and a detail layer image. A first neural network uses the intensity image to predict statistics of the SDR image in a higher dynamic range. These predicted statistics together with the original image statistics of the input image are used to derive an optimal tone-mapping curve to map the input SDR image onto an HDR display. Optionally, a second neural network, using the intensity image and the detail layer image, can generate a residual detail layer image in a higher dynamic range to enhance the tone-mapping of the base layer image into the higher dynamic range.

    Neural networks for high dynamic range video super-resolution

    公开(公告)号:US12283023B1

    公开(公告)日:2025-04-22

    申请号:US18846430

    申请日:2023-03-17

    Abstract: Methods and systems for the super resolution of high dynamic range (HDR) video are described. Given a sequence of video frames, a current frame and two or more neighboring frames are processed by a neural-network (NN) feature extraction module, followed by a NN upscaling module, and a NN reconstruction module. In parallel, the current frame is upscaled using traditional up-sampling to generate an intermediate up-sampled frame. The output of the reconstruction module is added to the intermediate up-sampled frame to generate an output frame. Additional traditional up-sampling may be performed on the output frame to match the desired up-scaling factor, beyond the up-scaling factor for which the neural network was trained.

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