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240120s2024 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2024.3356188
|2 doi
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|a eng
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|a Dudhane, Akshay
|e verfasserin
|4 aut
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|a Burst Image Restoration and Enhancement
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|c 2024
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|a Date Revised 23.01.2024
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|a Citation Status Publisher
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|a Burst Image Restoration aims to reconstruct a high-quality image by efficiently combining complementary inter-frame information. However, it is quite challenging since individual burst images often have inter-frame misalignments that usually lead to ghosting and zipper artifacts. To mitigate this, we develop a novel approach for burst image processing named BIPNet that focuses solely on the information exchange between burst frames and filter-out the inherent degradations while preserving and enhancing the actual scene details. Our central idea is to generate a set of pseudo-burst features that combine complementary information from all the burst frames to exchange information seamlessly. However, due to inter-frame misalignment, the information cannot be effectively combined in pseudo-burst. Thus, we initially align the incoming burst features regarding the reference frame using the proposed edge-boosting feature alignment. Lastly, we progressively upscale the pseudo-burst features in multiple stages while adaptively combining the complementary information. Unlike the existing works, that usually deploy single-stage up-sampling with a late fusion scheme, we first deploy a pseudo-burst mechanism followed by the adaptive-progressive feature up-sampling. The proposed BIPNet significantly outperforms the existing methods on burst super-resolution, low-light image enhancement, low-light image super-resolution, and denoising tasks. The pre-trained models and source code are available at https://github.com/akshaydudhane16/BIPNet
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|a Journal Article
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|a Zamir, Syed Waqas
|e verfasserin
|4 aut
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|a Khan, Salman
|e verfasserin
|4 aut
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| 700 |
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|a Khan, Fahad Shahbaz
|e verfasserin
|4 aut
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|a Yang, Ming-Husan
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g PP(2024) vom: 19. Jan.
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|x 1939-3539
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|u http://dx.doi.org/10.1109/TPAMI.2024.3356188
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