Real-world blind super-resolution using stereoscopic feature and coupled optimization.

Abstract

Blind super-resolution is typically decomposed into degradation estimation and image restoration to mitigate the ill-condition. Most existing methods employ two independent models to address these two sub-problems separately. However, independent models fail to fully account for the correlation between degradation and image, leading to incompatibilities and subsequent performance decline. Additionally, numerous algorithms leverage convolutional neural networks (CNNs) for degradation estimation, which is inadequate for capturing degradation information with global semantics. Based on the problems above, we propose a novel Coupled Optimization Strategy (COS) and a stereoscopic feature processing block. Considering the correlation between degraded images and corresponding degradation parameters, COS solves two sub-problems using a single model, enabling the problem to be optimized within a unified solution space. Meanwhile, a stereoscopic extraction structure capable of capturing local, global, and locally-global fused features is developed to efficiently implement COS and accommodate the bimodality of blind super-resolution. Extensive experiments on real and synthetic datasets validate the effectiveness of our method, yielding a 0.2 dB gain in PSNR on the DIV2KRK dataset with scale factor 2, compared to state-of-the-art algorithms.