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展现了有效性能。然而实际应用中的退化往往未知，模型与退化之间的失配会导致严重的性能下降。本文提出一种可处理多种退化的全能型图像复原网络。由于不同类型退化具有异质性，在单一网络中处理多种退化颇具挑战。为此，我们提出学习神经退化表征（NDR），该表征能够捕捉各种退化的内在特征。所学习的NDR可自适应地分解不同类型退化，其作用类似于表示基本退化成分的神经字典。基于此，我们进一步开发退化查询模块与退化注入模块，通过NDR有效识别并利用特定退化信息，从而实现面向多种退化的全能复原能力。此外，我们提出双向优化策略，通过交替优化退化与复原过程，有效驱动NDR学习退化表征。在典型退化类型（包括噪声、雾霾、雨纹和下采样）上的综合实验证明了我们方法的有效性与泛化能力。