Lightweight bidirectional feedback network for image super-resolution

Abstract

Super-resolution has attracted academic attention recently, for its capabilities of image restoration and image enhancement. To generate informative high-level features for a better reconstruction performance, most super-resolution networks have many parameters, which limit their application in resource-constrained devices. Feedback networks can generate informative high-level features with few parameters by feeding high-level features back to previous layers. In this paper, we propose a lightweight bidirectional feedback network for image super-resolution (LBFN), which consists of two feedback procedures connected in reverse. Bidirectional feedback architecture further improves the perceptual abilities of feedback networks by fusing different level features sufficiently. We propose a residual attention block to enhance the detail expression ability of feature maps, which are cross-learned in feedback block. Finally, we propose a SR regression loss to supervise the training of our network. Extensive experiments demonstrate that our method has an outstanding performance while taking up little computing resources.