Cascading and Residual Connected Network for Single Image Superresolution

Convolution neural networks facilitate the significant process of single image super-resolution (SISR). However, most of the existing CNN-based models suffer from numerous parameters and excessively deeper structures. Moreover, these models relying on in-depth features commonly ignore the hints of low-level features, resulting in poor performance. This paper demonstrates an intriguing network for SISR with cascading and residual connections (CASR), which alleviates these problems by extracting features in a small net called head module via the strategies based on the depthwise separable convolution and deformable convolution. Moreover, we also include a cascading residual block (CAS-Block) for the upsampling process, which benefits the gradient propagation and feature learning while easing the model training. Extensive experiments conducted on four benchmark datasets demonstrate that the proposed method is superior to the latest SISR methods in terms of quantitative indicators and realistic visual effects.