  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.
