Not All Degradations Are Equal: A Targeted Feature Denoising Framework for Generalizable Image Super-Resolution

Generalizable Image Super-Resolution aims to enhance model generalization capabilities under unknown degradations. To achieve such goal, the models are expected to focus only on image content-related features instead of degradation details (i.e., overfitting degradations).Recently, numerous approaches such as dropout and feature alignment have been proposed to suppress models' natural tendency to overfitting degradations and yields promising results. Nevertheless, these works have assumed that models overfit to all degradation types (e.g., blur, noise), while through careful investigations in this paper, we discover that models predominantly overfit to noise, largely attributable to the distinct degradation pattern in noise compared to other degradation types. In this paper, we propose a targeted feature denoising framework, comprising noise detection and denoising modules. Our approach represents a general solution that can be seamlessly integrated with existing super-resolution models without requiring architectural modifications. Our framework demonstrates superior performance compared to previous regularization-based methods across five traditional benchmark and datasets, encompassing both synthetic and real-world scenarios.