Cascade Attention Blend Residual Network For Single Image Super-Resolution

Nowadays, deep convolutional neural networks are playing an increasingly important role in single-image super-resolution vision applications. Yet, most of the existing deep convolution-based methodologies are insufficiently intelligent to capture targeted information when the distribution of spatial and channel information is uneven for low-resolution images. To address this research issue, we propose a cascade attention blend residual network, with the non-local channel and multi-scale attention being considered for channel-wise dependencies and multi-scale receptive fields, respectively. Cascading both attentions in a potent blend residual block aims to learn more spatial and channel correlations between low- and super-resolution images. Experimental results demonstrate that the proposed method achieves promising performance for super-resolution image reconstruction, as well as gains an average reduction of 50.9% network parameters, compared to some state-of-the-art methods.