KNOWLEDGE DISTILLATION BASED DEGRADATION ESTIMATION FOR BLIND SUPER-RESOLUTION

Abstract

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 groundtruth. 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 code is available at Github.

1. INTRODUCTION

Single image super-resolution (SISR) aims to recover details of a high-resolution (HR) image from its low-resolution (LR) counterpart, which has a variety of downstream applications (Dong et al., 2014; Zhang et al., 2019; Xia et al., 2022d; Fritsche et al., 2019; Xia et al., 2022c; b) . These stateof-the-art methods (Kim et al., 2016; Lim et al., 2017; Lai et al., 2017; Xia et al., 2022a; Wang et al., 2018b) usually assume that there is an ideal bicubic downsampling kernel to generate LR images. However, this simple degradation is different from more complex degradations existing in real-world LR images. This degradation mismatch will lead to severe performance drops. To address the issue, blind super-resolution (Blind-SR) methods are developed. Some Blind-SR works (Wang et al., 2021a; Luo et al., 2022) use the classical image degradation process, given by Eq. 1. Recently, some works (Cai et al., 2019; Bulat et al., 2018) attempted to develop a new and complex degradation process to better cover real-world degradation space, which forms a variant of Blind-SR called real-world super-resolution (Real-SR). The representative works include BSR-GAN (Zhang et al., 2021) and Real-ESRGAN (Wang et al., 2021b) , which introduce comprehensive degradation operations such as blur, noise, down-sampling, and JPEG compression, and control the severity of each operation by randomly sampling the respective hyper-parameters. To better simulate the complex degradations in real-world, they also apply random shuffle of degradation orders (Zhang et al., 2021) and second-order degradation (Wang et al., 2021b) respectively. Since Blind-SR faces almost infinite degradations, introducing prior degradation information to SR networks can help to constrain the solution space and boost SR performance. As shown in Fig. 1 , the way to obtain degradation information can be divided into three categories: (1) Several Non-Blind SR methods (Zhang et al., 2018a; Shocher et al., 2018; Zhang et al., 2020; Soh et al., 2020; Xu et al., 2020) directly take the known degradation information as prior (Fig. 1 



(a)). (2) Most of

