All-In-One Image Restoration for Unknown Corruption

By Boyun Li, Xiao Liu, Peng Hu, Zhongqin Wu, Jiancheng Lv, Xiaocui Peng. 2022

572 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Regionwise Generative Adversarial Image Inpainting for Large Missing Areas (2019, background): “[45] designed an AirNet to study a challenging problem in image restoration, which recovers images from a variety of unknown corruption types and levels.”
Restoring Vision in Adverse Weather Conditions With Patch-Based Denoising Diffusion Models (2022, background): “this problem for multiple weather corruptions in unified architectures [7], [9], [10], [11].”