Joint restoration convolutional neural network for low-quality image super resolution

Abstract

In this paper, a joint restoration convolutional neural network (JRCNN) is proposed to produce a visually pleasing super resolution (SR) image from a single low-quality (LQ) image. The LQ image is a low resolution (LR) image with ringing, blocking and blurring artifacts arising due to compression. JRCNN consists of three deep dense residual blocks (DRB). Each DRB comprises of parallel convolutional layers with cross residual connections. The representational power of JRCNN is improved by depth-wise concatenation of feature representations from each of the DRBs. Moreover, these connections mitigate the problem of vanishing of gradients. Different from the previous networks, JRCNN exploits the contextual information directly in the LR image space without using any interpolation. This strategy improves the training efficiency of the network. The exhaustive experimentation on different datasets show that the proposed JRCNN produces state-of-the-art performance. Furthermore, ablation experiments are performed to assess the effectiveness of JRCNN. In addition, individual experiments are conducted for SR and compression artifact removal on benchmark datasets.