Blind super-resolution (BSR) methods based on high-resolution (HR) reconstruction codebooks have achieved promising results in recent years. However, we find that a codebook based on HR reconstruction may not effectively capture the complex correlations between low-resolution (LR) and HR images. In detail, multiple HR images may produce similar LR versions due to complex blind degradations, causing the HR-dependent only codebooks having limited texture diversity when faced with confusing LR inputs. To alleviate this problem, we propose the Rich Texture-aware Codebook-based Network (RTCNet), which consists of the Degradation-robust Texture Prior Module (DTPM) and the Patch-aware Texture Prior Module (PTPM). DTPM effectively mines the cross-resolution correlation of textures between LR and HR images by exploiting the cross-resolution correspondence of textures. PTPM uses patch-wise semantic pre-training to correct the misperception of texture similarity in the high-level semantic regularization. By taking advantage of this, RTCNet effectively gets rid of the misalignment of confusing textures between HR and LR in the BSR scenarios. Experiments show that RTCNet outperforms state-of-the-art methods on various benchmarks by up to 0.16 ~ 0.46dB.

翻译：盲超分辨率（BSR）方法基于高分辨率（HR）重建码本近年来取得了显著成果。然而，我们发现基于HR重建的码本可能无法有效捕捉低分辨率（LR）和HR图像之间的复杂相关性。具体而言，由于复杂的盲退化过程，多个HR图像可能产生相似的LR版本，导致仅依赖HR的码本在面临模糊LR输入时纹理多样性受限。为缓解此问题，我们提出富含纹理感知码本网络（RTCNet），该网络包含退化鲁棒纹理先验模块（DTPM）和块感知纹理先验模块（PTPM）。DTPM通过利用纹理的跨分辨率对应关系，有效挖掘LR与HR图像之间的跨分辨率纹理相关性；PTPM则采用分块语义预训练来修正高层语义正则化中对纹理相似性的误判。借助这一优势，RTCNet在BSR场景下有效消除了HR与LR之间纹理混淆的错位问题。实验表明，RTCNet在多个基准测试上以高达0.16~0.46dB的性能优势超越现有最优方法。