Deep convolution-based single image super-resolution (SISR) networks embrace
the benefits of learning from large-scale external image resources for local
recovery, yet most existing works have ignored the long-range feature-wise
similarities in natural images. Some recent works have successfully leveraged
this intrinsic feature correlation by exploring non-local attention modules.
However, none of the current deep models have studied another inherent property
of images: cross-scale feature correlation. In this paper, we propose the first
Cross-Scale Non-Local (CS-NL) attention module with integration into a
recurrent neural network. By combining the new CS-NL prior with local and
in-scale non-local priors in a powerful recurrent fusion cell, we can find more
cross-scale feature correlations within a single low-resolution (LR) image. The
performance of SISR is significantly improved by exhaustively integrating all
possible priors. Extensive experiments demonstrate the effectiveness of the
proposed CS-NL module by setting new state-of-the-arts on multiple SISR
benchmarks.

本论文提出了首个可将跨尺度特征相关性与非局部注意力机制相融合的循环神经网络，该模型及其与本地和同尺度非局部注意力结合可以更好地发现单个低分辨率图像中的跨尺度特征相关性，从而显著提高图像超分辨率的性能，并在多项 SISR 基准测试中表现出新的最佳效果。