Abstract
CNNs facilitate the significant process of single-image super-resolution (SISR). However, most of the existing CNN-based models suffer from numerous parameters and exceeding deeper structures. Moreover, these models relying on deep features commonly ignore the hints of low-level features, resulting in poor performance. In this paper, we demonstrate an effective network named CASR, which addresses these problems by extracting features in Head Module via the strategies based on the depth-wise separable convolution and includes a cascading residual Block (CAS-Block) for the upsampling process, which benefits the gradient propagation and feature learning while eases the model training. Extensive experiments conducted on four benchmark datasets demonstrate the proposed method outperforms the state-of-the-art methods for SISR.
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