Self-similarity is valuable to the exploration of non-local textures in single image super-resolution (SISR). Researchers usually assume that the importance of non-local textures is positively related to their similarity scores. In this paper, we surprisingly found that when repairing severely damaged query textures, some non-local textures with low-similarity which are closer to the target can provide more accurate and richer details than the high-similarity ones. In these cases, low-similarity does not mean inferior but is usually caused by different scales or orientations. Utilizing this finding, we proposed a Global Learnable Attention (GLA) to adaptively modify similarity scores of non-local textures during training instead of only using a fixed similarity scoring function such as the dot product. The proposed GLA can explore non-local textures with low-similarity but more accurate details to repair severely damaged textures. Furthermore, we propose to adopt Super-Bit Locality-Sensitive Hashing (SB-LSH) as a preprocessing method for our GLA. With the SB-LSH, the computational complexity of our GLA is reduced from quadratic to asymptotic linear with respect to the image size. In addition, the proposed GLA can be integrated into existing deep SISR models as an efficient general building block. Based on the GLA, we constructed a Deep Learnable Similarity Network (DLSN), which achieves state-of-the-art performance for SISR tasks of different degradation types (e.g. blur and noise). Our code and a pre-trained DLSN have been uploaded to GitHub{\dag} for validation.

翻译：自我相似性对于在单一图像超分辨率(SISR)中探索非本地纹理很有价值。 研究人员通常认为非本地纹理的重要性与其相似性分数有着积极的关系。 在本文中,我们惊讶地发现,当修复严重损坏的查询纹理时,一些与目标相近的、与目标相近的非本地纹理可以提供比高相似性更准确和更丰富的细节。在这些案例中,低偏差并不意味着低差,而是通常由不同规模或方向造成。 利用这一发现,我们建议全球可学习注意(GLA)在培训期间对非本地纹理的相似性分数进行适应性修改,而不是仅仅使用固定的类似评分功能,例如点产品。 拟议的GLA可以探索与目标相近但更准确的细节来修复严重损坏的纹理。 此外, 我们提议采用超小型本地定律(SB- LS- LL), 将我们GLA 的透明性变异性变码性化为我们GLA 的常规结构, 将SL 变缩缩变为GL。