图像复原
计算机科学
图像(数学)
灵活性(工程)
推论
降级(电信)
人工智能
编码(集合论)
编码器
语言变化
先验概率
计算机视觉
图像处理
数学
贝叶斯概率
统计
艺术
文学类
集合(抽象数据类型)
操作系统
程序设计语言
电信
作者
Boyun Li,Xiao Liu,Peng Hu,Zhongqin Wu,Jiancheng Lv,Xi Peng
标识
DOI:10.1109/cvpr52688.2022.01693
摘要
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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