A Direction-Decoupled Non-Local Attention Network for Single Image Super-Resolution
Zijiang Song, Baojiang Zhong, Jiahuan Ji, Kai‐Kuang Ma · IEEE Signal Processing Letters · 2022
Thenon-local attentionmechanism has often been exploited in deep learning to capturelong-range dependencies(LRDs) from the same image for enhancing the performance of various image processing methods. However, the initially proposed non-local attention process inevitably yields extremely-high computation complexity, sinceallthe feature points are involved in computing the LRDs. To address this concern, a recently proposedcriss-cross network(CCNet), which has arecurrent criss-cross attention(RCCA) module, is used to compute the LRDs by involving only a small set of feature points for significantly reducing computation. Motivated by the RCCA, a noveldirection-decoupled non-local attention(DNA) module is proposed in this paper that is able to further reduce the computation complexity of RCCA by half approximately. To verify the performance of our new non-local attention module, a DNA network is developed for conducting single image super-resolution (SISR). Extensive experimental results have clearly demonstrated the superiority of using our DNA network for SISR when compared with that of state-of-the-art methods.