Texture-centralized deep convolutional neural network for single image super resolution
Chengqi Li, Zhigang Ren, Bo Yang, Xingyu Wan, Jinjun Wang · 2017
There have been significant progresses in single image super-resolution (SR) using deep convolutional neural network. In this paper, we propose a modified deep convolutional neural network model incorporated with image texture priors for single image SR. The model consist of a particular feature extraction layer followed by image reconstruction process, aiming to centralize on the image texture information so as to make the overall SR task more effective. This proposal is compared with current state-of-the-art methods on standard images. Our experimental results confirmed that, incorporating image texture prior information with conventional high-resolution image reconstruction process can lead to better performance and faster convergence speed simultaneously.