Existing methods have demonstrated effective performance on a single degradation type. In practical applications, however, the degradation is often unknown, and the mismatch between the model and the degradation will result in a severe performance drop. In this paper, we propose an all-in-one image restoration network that tackles multiple degradations. Due to the heterogeneous nature of different types of degradations, it is difficult to process multiple degradations in a single network. To this end, we propose to learn a neural degradation representation (NDR) that captures the underlying characteristics of various degradations. The learned NDR decomposes different types of degradations adaptively, similar to a neural dictionary that represents basic degradation components. Subsequently, we develop a degradation query module and a degradation injection module to effectively recognize and utilize the specific degradation based on NDR, enabling the all-in-one restoration ability for multiple degradations. Moreover, we propose a bidirectional optimization strategy to effectively drive NDR to learn the degradation representation by optimizing the degradation and restoration processes alternately. Comprehensive experiments on representative types of degradations (including noise, haze, rain, and downsampling) demonstrate the effectiveness and generalization capability of our method.

翻译：现有方法在单一退化类型上已展现出有效性能。然而，在实际应用中，退化类型往往是未知的，模型与退化类型之间的不匹配将导致严重的性能下降。本文提出一种面向多类退化的统一图像复原网络。由于不同类型退化之间存在异质性，在单一网络中处理多种退化具有挑战性。为此，我们提出学习一种神经退化表征，该表征能够捕捉各类退化的本质特征。学习得到的神经退化表征可自适应地分解不同类型的退化，其作用类似于表示基础退化分量的神经字典。随后，我们开发了退化查询模块与退化注入模块，以基于神经退化表征有效识别并利用特定退化，从而实现对多种退化的统一复原能力。此外，我们提出一种双向优化策略，通过交替优化退化过程与复原过程，有效驱动神经退化表征学习退化特征。在典型退化类型（包括噪声、雾霾、雨滴及下采样）上的综合实验验证了本方法的有效性与泛化能力。