Adsrnet: Attention-based densely connected network for image super-resolution

Abstract

Densely connected network for Image Super-Resolution SR has achieved much better results than most of the other methods owing to its dense connection architecture which can provide more and deeper features for image super-resolution. However, since the dense block accepts the outputs of all previous blocks, it receives a lot of redundant and conflicting information, which results in longer training time and bad superresolution reconstruction results. To solve this problem, we introduce an attention module into a densely connected network and propose an attention-based densely connected network ADSRNet for image superresolution. With the attention module, our ADSRNet can select more important information and cut off those redundant for image superresolution from a large number of feature maps by importance ordering. Thus, we can speed up the training of network. Extensive experiments are performed over the datasets Set5, Set14 and BSD100, the qualitatively and quantitatively evaluated results for our proposed ADSRNet are better than ones of some state-of-the-art methods.