The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
All-In-One Image Restoration for Unknown Corruption
Abstract
In this paper, we study a challenging problem in image restoration, namely, how to develop an all-in-one method that could recover images from a variety of unknown corruption types and levels. To this end, we propose an All-in-one Image Restoration Network (AirNet) consisting of two neural modules, named Contrastive-Based Degraded Encoder (CBDE) and Degradation-Guided Restoration Network (DGRN). The major advantages of AirNet are two-fold. First, it is an all-in-one solution which could recover various degraded images in one network. Second, AirNet is free from the prior of the corruption types and levels, which just uses the observed corrupted image to perform inference. These two advantages enable AirNet to enjoy better flexibility and higher economy in real world scenarios wherein the priors on the corruptions are hard to know and the degradation will change with space and time. Extensive experimental results show the proposed method outperforms 17 image restoration baselines on four challenging datasets. The code is available at https://github.com/XLearning-SCU/2022-CVPR-AirNet.
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Momentum Contrast for Unsupervised Visual Representation Learning
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A simple framework for contrastive learning of visual representations
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Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
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Single image haze removal using dark channel prior
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Deformable Convolutional Networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.89 · Cited by 7,513
Representation Learning with Contrastive Predictive Coding
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Unsupervised Feature Learning via Non-parametric Instance Discrimination
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Self-citation
Metadata
- Authors
- Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., & Peng, X.
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 17431-17441
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4312373367
- Cited by
- 432
- References
- 54
- FWCI
- 29.55
- Type
- conference paper
- Language
- en
- Open access
- closed
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