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Titlebook: Computer Vision – ECCV 2022 Workshops; Tel Aviv, Israel, Oc Leonid Karlinsky,Tomer Michaeli,Ko Nishino Conference proceedings 2023 The Edit

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樓主: Debilitate
51#
發(fā)表于 2025-3-30 09:30:06 | 只看該作者
52#
發(fā)表于 2025-3-30 15:09:06 | 只看該作者
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發(fā)表于 2025-3-30 17:11:02 | 只看該作者
Discussion of Results and Conclusions,etter visual quality. Experimental results show that the proposed UDC-UNet outperforms the state-of-the-art methods in quantitative and qualitative comparisons. Our approach won second place in the UDC image restoration track of the MIPI challenge. Codes and models are available at ..
54#
發(fā)表于 2025-3-30 21:06:27 | 只看該作者
Theodore P. Williams,B. N. Bakerhe existing state-of-the-art methods both qualitatively and visually, and our ECFNet achieves the best performance in terms of all the evaluation metrics in the MIPI-challenge 2022 Under-display Camera Image Restoration track. Our source codes are available at ..
55#
發(fā)表于 2025-3-31 01:59:18 | 只看該作者
56#
發(fā)表于 2025-3-31 07:14:20 | 只看該作者
Robert B. Barlow Jr.,Steven C. Chamberlainnal Processor (ISP) to generate the RGB images from our reconstructed bayers. With the use of color information in the second stage, the quality of reconstructed bayers is further improved. Moreover, we propose a data pre-processing method including data augmentation and bayer rearrangement. The exp
57#
發(fā)表于 2025-3-31 10:07:29 | 只看該作者
Advances in Spatial and Network Economicsency domain, we propose a Global-Local Refinement Network (GLRN) to estimate depth residuals separately at each scale. Furthermore, to compensate for the structural information lost by coarse depth map downsampling and further optimize the results with the color image, we propose a novel and efficie
58#
發(fā)表于 2025-3-31 13:36:33 | 只看該作者
59#
發(fā)表于 2025-3-31 19:38:56 | 只看該作者
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