Deep‐network based method for joint image deblocking and super‐resolution. Issue 10 (16th July 2019)

• Record Type: Journal Article
• Authors: Li, Xiaoguang; Sun, Xu; Man Lam, Kin; Zhuo, Li; Li, Jiafeng; Dong, Ning
• Is Part Of: IET image processing. Volume 13:Issue 10(2019)
• Page Start: 1636
• Page End: 1647
• Publication Date: 2019-07-16
• Subjects: neural nets -- image restoration -- image resolution; deep‐network based method -- joint image deblocking -- super‐resolution -- high‐quality images -- low‐quality versions -- single degraded factor -- degradation factors -- compression distortion -- image acquisition -- joint deep‐network‐based image‐restoration algorithm -- convolutional neural network -- deblocking network -- cascade deblocking subnets -- end‐to‐end training scheme -- general images -- face images -- deep network; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.6113
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Many pieces of research have been conducted on image‐restoration techniques to recover high‐quality images from their low‐quality versions, but they usually aim to handle a single degraded factor. However, captured images usually suffer from various degradation factors, such as low resolution and compression distortion, in the procedures of image acquisition, compression, and transmission simultaneously. Ignoring the correlation of different degraded factors may result in the limited efficiency of the existing image‐restoration methods for captured images. A joint deep‐network‐based image‐restoration algorithm is proposed to establish a restoration framework for image deblocking and super‐resolution. The proposed convolutional neural network is made up of two stages. A deblocking network is constructed with two cascade deblocking subnets first, then, super‐resolution is performed by a very deep network with skipping links. Cascading these two stages forms a novel deep network. An end‐to‐end training scheme is developed, which makes the two stages be trained jointly so as to achieve better performance. Intensive evaluations have been conducted to measure the performance of the authors' method both in general images and face images. Experimental results on several datasets demonstrate that the proposed method outperforms other state‐of‐the‐art methods, in terms of both subjective and objective performances.