Iterative convolutional neural network for noisy image super-resolution
Wenbo Bao, Xiaoyun Zhang, Shangpeng Yan, Zhiyong Gao · 2017
Images captured by camera tend to be noisy and their qualities are often deteriorated in super-resolution. In this paper, we propose an end-to-end convolutional neural network to generate denoised, high-resolution image directly from its noisy, low-resolution counterpart. To preserve textures and eliminate noises simultaneously, the network is organized into an iterative structure for the recovery of high-quality image step by step. Each step of the structure is aimed to learn a better result with reference of its predecessor's output. Experiments show that our method is able to produce more desirable highresolution images in both objective and subjective evaluations comparing to conventional ones as well as non-iterative network based one.