Blind image super-resolution (Blind-SR) aims to recover a high-resolution (HR) image from its corresponding low-resolution (LR) input image with unknown degradations. Most of the existing works design an explicit degradation estimator for each degradation to guide SR. However, it is infeasible to provide concrete labels of multiple degradation combinations (e.g., blur, noise, jpeg compression) to supervise the degradation estimator training. In addition, these special designs for certain degradation, such as blur, impedes the models from being generalized to handle different degradations. To this end, it is necessary to design an implicit degradation estimator that can extract discriminative degradation representation for all degradations without relying on the supervision of degradation ground-truth. In this paper, we propose a Knowledge Distillation based Blind-SR network (KDSR). It consists of a knowledge distillation based implicit degradation estimator network (KD-IDE) and an efficient SR network. To learn the KDSR model, we first train a teacher network: KD-IDE$_{T}$. It takes paired HR and LR patches as inputs and is optimized with the SR network jointly. Then, we further train a student network KD-IDE$_{S}$, which only takes LR images as input and learns to extract the same implicit degradation representation (IDR) as KD-IDE$_{T}$. In addition, to fully use extracted IDR, we design a simple, strong, and efficient IDR based dynamic convolution residual block (IDR-DCRB) to build an SR network. We conduct extensive experiments under classic and real-world degradation settings. The results show that KDSR achieves SOTA performance and can generalize to various degradation processes. The source codes and pre-trained models will be released.

翻译：盲人图像超分辨率( Blind- SR) 旨在从相应的低分辨率( LR) 输入图像中恢复高分辨率( HR) 图像, 并具有未知的降解性。 大多数现有作品都设计了一个清晰的降解估计器, 以指导SR。 然而, 无法提供多种降解组合( 如模糊、 噪音、 jpeg 压缩) 的具体标签, 以监督降解估计器培训。 此外, 这些针对某些降解的特殊设计, 如模糊, 阻碍模型被普遍推广, 处理不同的降解。 为此, 有必要设计一个隐含的降解估计器, 用于为每降解设计一个有区别的降解表示器, 而无需依靠对降解地图的监管。 但是, 我们建议一个基于知识蒸馏的隐性降解估测器网络( KD- ID) 和一个高效的SR( 学习KD- DR) 模式, 我们首先培训一个强大的教师网络: KD- DD$ 和 将一个基于 不断变现的变现的 KDR 。