Two Heads Better than One: Dual Degradation Representation for Blind Super-Resolution

TL;DR

This paper introduces a dual-branch network that predicts separate embeddings for blur and noise in degraded images, enabling improved blind super-resolution performance across benchmarks.

Contribution

It proposes a novel dual degradation extractor that models blur and noise separately, enhancing adaptability and accuracy in blind super-resolution tasks.

Findings

Achieves state-of-the-art results on multiple benchmarks.

Effectively models both blur and noise in degradation.

Improves robustness over existing blind SR methods.

Abstract

Previous methods have demonstrated remarkable performance in single image super-resolution (SISR) tasks with known and fixed degradation (e.g., bicubic downsampling). However, when the actual degradation deviates from these assumptions, these methods may experience significant declines in performance. In this paper, we propose a Dual Branch Degradation Extractor Network to address the blind SR problem. While some blind SR methods assume noise-free degradation and others do not explicitly consider the presence of noise in the degradation model, our approach predicts two unsupervised degradation embeddings that represent blurry and noisy information. The SR network can then be adapted to blur embedding and noise embedding in distinct ways. Furthermore, we treat the degradation extractor as a regularizer to capitalize on differences between SR and HR images. Extensive experiments on several benchmarks demonstrate our method achieves SOTA performance in the blind SR problem.