Blind super-resolution (SR) aims to reconstruct high-resolution images from low-quality inputs under unknown degradation conditions. While numerous blind SR methods have been proposed in recent years, they still face critical limitations. Most approaches perform well under specific degradation patterns but struggle with complex scenarios involving multiple degradation factors and varying noise levels. This often leads to loss of structural integrity and fine details, resulting in suboptimal restoration quality. Furthermore, existing methods typically rely on convolutional neural networks (CNNs) with limited receptive fields, which hinders effective cross-domain information integration. Their inability to capture long-range dependencies compromises the reconstruction of global structures. In edge detection, conventional techniques frequently produce inaccurate or false edges, further degrading the quality of cross-domain integration and image restoration. To address these challenges, we propose SITFFormer (Structural Integrity and Texture Fidelity Transformer), a novel transformer-based framework for blind single-image SR. Our approach incorporates the Canny edge detection algorithm to accurately preserve true edges and suppress noise-induced artifacts, enhancing edge localization in complex and noisy environments. We also introduce the Cross-Domain Structure-Texture-aware Network (CDSTNet), designed to integrate intra-domain and cross-domain features for comprehensive structure preservation and texture recovery. CDSTNet comprises two key modules: Cross-Domain Integration (CDI): Fuses intra- and cross-domain features to retain structural and textural details. Cross-Domain Learnable Attention (CDLA): Explores global dependencies, adaptively refines feature similarity, and filters out redundant non-local information. Both modules are equipped with a Cross-Attention mechanism (CAM) to facilitate effective interaction and complementarity between domains, enhancing reconstruction fidelity. Extensive experiments on synthetic, noisy, and real-world datasets demonstrate that SITFFormer surpasses state-of-the-art methods in quantitative performance and visual quality, particularly in preserving structural integrity and recovering fine textures.
Hsu et al. (Sat,) studied this question.