Single Image Super-Resolution Based on Cascaded Recursive Residual Convolutional Neural Network
Chao Wang, Wen Gao, Xiaorui Guo, Jianping Qiao, Chunxing Wang · 2019
The accurate and automatic reconstruction of a super-resolution image from one single low-resolution image is an important challenging problem for image analysis. In this paper, we have optimized the problems of existing methods and presented an automated super-resolution image reconstruction system from a single low-resolution image based on cascaded recursive residual convolutional neural network. In this method, we extracted the characteristics of images firstly, then used recursive networks to connect the feature information of each layer to obtain three residual images. Finally, the high-resolution image was obtained by applying the deconvolution at the end of network. we used the recursive structure applying to the residual learning, this network differed from traditional residual learning in that the structure and overlay of the remaining learning network are different, it can solved the problem of gradient explosion, overfitting and insufficient transmission of high frequency component. Compared with present four methods, our algorithm had good performance in experiment of super-resolution testing. Experimental results showed that the proposed method could recover the fine details and textures, simultaneously, it has satisfactory performance in PSNR values.