2022年11月21日 · High dynamic range (HDR) image is widely-used in graphics and photography due to the rich information it contains. Recently the community has started using deep neural network (DNN) to reconstruct standard dynamic range (SDR) images into HDR. Albeit the superiority of current DNN-based methods, their application scenario is still limited: (1) heavy model impedes real-time processing, and (2 ...

The official PyTorch implementation of paper 'LHDR: HDR Reconstruction for Legacy Content using a Lightweight DNN' in ACCV2022. - AndreGuo/LHDR

LHDR: HDR Reconstruction for Legacy Content using a Lightweight DNN Cheng Guo1,2[0000−0002−2660−2267] and Xiuhua Jiang1,2 1 State Key Laboratory of Media Convergence and Communication, Communication University of China 2 Peng Cheng Laboratory, Shenzhen, China {guocheng, jiangxiuhua}@cuc.edu.cn Abstract. High dynamic range (HDR) image is widely-used in graphics

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LA-HDR is strongly suggested to be trained seperately by each sub-networks sequentially. As the whole solution is focused on HDR reconstruction, we do not put much efforts on low-light denoising, thus the denoising effect of DeNet is rather poor. The pre-trained model for DeNet is not provided in PyTorch version. Users may evaluate its performance in TensorFlow one.

2023年3月2日 · Image’s dynamic range is defined as the ratio of maximum recorded luminance to the minimum. As name implies, high dynamic range (HDR) image is able to simultaneously envelop rich information in both bright and dark areas, making it indispensable in photography and image-based lighting [], etc.The common way to obtain an HDR image is fusing multiple standard dynamic range (SDR) images with ...

Cheng Guo, Xiuhua Jiang; Proceedings of the Asian Conference on Computer Vision (ACCV), 2022, pp. 3155-3171

2022年12月4日 · High dynamic range (HDR) image is widely-used in graphics and photography due to the rich information it contains. Recently the community has started using deep neural network (DNN) to reconstruct standard dynamic range (SDR) images into HDR.

Deep HDR Video from Sequences with Alternating Exposures Eurographics 2019 | Paper. Single-frame Regularization for Temporally Stable CNNs CVPR 2019 | project. Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications

LHDR: HDR Reconstruction for Legacy Content using a Lightweight DNN, Supplementary Material Cheng Guo1,2[0000−0002−2660−2267] and Xiuhua Jiang1,2 1 State Key Laboratory of Media Convergence and Communication, Communication University of China 2 Peng Cheng Laboratory, Shenzhen, China {guocheng, jiangxiuhua}@cuc.edu.cn