An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution

Wang Wei, Jiang Yongbin, Luo Yanhong, Li Ji, Wang Xin, Zhang Tong
2019 International Journal of Computational Intelligence Systems  
A B S T R A C T In recent years, more and more attention has been paid to single image super-resolution reconstruction (SISR) by using deep learning networks. These networks have achieved good reconstruction results, but how to make better use of the feature information in the image, how to improve the network convergence speed, and so on still need further study. According to the above problems, a novel deep residual dense network (DRDN) is proposed in this paper. In detail, DRDN uses the
more » ... DRDN uses the residual-dense structure for local feature fusion, and finally carries out global residual fusion reconstruction. Residual-dense connection can make full use of the features of low-resolution images from shallow to deep layers, and provide more low-resolution image information for super-resolution reconstruction. Multi-hop connection can make errors spread to each layer of the network more quickly, which can alleviate the problem of difficult training caused by deepening network to a certain extent. The experiments show that DRDN not only ensure good training stability and successfully converge but also has less computing cost and higher reconstruction efficiency. was significantly improved compared with the traditional learning algorithm. With the success of deep neural network in ImageNet [11, 12] , the application of deep neural network in image super-resolution reconstruction has become an important research content. Accurate Image Super-Resolution (VDSR) [13] deepened the depth of convolutional neural network to 20 layers. In order to ensure the effective convergence of deep neural network in training, VDSR introduced global residual connection and gradient clipping technology. Inspired by the residual network model, deeply-recursive convolutional network (DRCN) [14] and deep recursive residual network (DRRN) [15] were proposed one after another. In addition to deepening the network, efficient sub-pixel convolutional neural network (ESPCN) [16] convolved directly on LR images, and finally used sub-pixel convolution layer to realize up-sampling process. Densely connected convolutional networks (DenseNet) [17] maximized the transmission of feature information between layers by densely connecting and the dense connection made full use of the feature maps obtained by each convolutional layer. Inspired by DenseNet, Tong et al. [18] firstly introduced dense network to realize super-resolution image reconstruction. Besides deepening the network, many researchers also proposed other structures to get better reconstruction results. Dong et al. [19] proposed a compact hourglass-shape CNN structure, namely FSRCNN, for faster and better SR, and re-designed the SRCNN structure mainly in three aspects. proposed the Laplacian Pyramid Super-Resolution Network (LapSRN). They
doi:10.2991/ijcis.d.191209.001 fatcat:4b6fsvdvhraahl6k62ztv2ltby