Image super-resolution is a challenging task and has attracted increasing attention in research and industrial communities. In this paper, we propose a novel end-to-end Attention-based DenseNet with Residual Deconvolution named as ADRD. In our ADRD, a weighted dense block, in which the current layer receives weighted features from all previous levels, is proposed to capture valuable features rely in dense layers adaptively. And a novel spatial attention module is presented to generate a group of attentive maps for emphasizing informative regions. In addition, we design an innovative strategy to upsample residual information via the deconvolution layer, so that the high-frequency details can be accurately upsampled. Extensive experiments conducted on publicly available datasets demonstrate the promising performance of the proposed ADRD against the state-of-the-arts, both quantitatively and qualitatively.
@article{arxiv.1907.05282,
title = {Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution},
author = {Zhuangzi Li},
journal= {arXiv preprint arXiv:1907.05282},
year = {2019}
}