Deep-Network based Method for Joint Image Deblocking and Super-Resolution
Deep-Network based Method for Joint Image Deblocking and Super-Resolution
复制标题
基于深度网络的联合图像去块和超分辨率方法
DOI：
10.1049/iet-ipr.2018.6113
复制
发表时间：
2019
影响因子：
2.3
通讯作者：
Ning Dong
中科院分区：
文献类型：
--
作者：
Xiaoguang Li;Xu Sun;Kin-Man Lam;Li Zhuo;Jiafeng Li;Ning Dong
许多研究已经进行了图像恢复技术，以恢复高质量的图像，从他们的低质量版本，但他们通常旨在处理一个单一的退化因素。然而，在图像采集、压缩和传输的过程中，图像往往会受到各种因素的影响，如分辨率低、压缩失真等。忽略不同退化因素的相关性可能会导致现有图像恢复方法对捕获图像的效率有限。提出了一种基于深度网络的联合图像恢复算法，建立了图像去块效应和超分辨率的恢复框架。所提出的卷积神经网络由两个阶段组成。首先用两个级联的去块效应网络构建去块效应网络，然后用一个具有跳链的深度网络进行超分辨率。级联这两个阶段形成了一个新的深度网络。提出了一种端到端的训练方案，使两个阶段联合训练，以达到更好的性能。已经进行了密集的评估，以衡量作者的方法在一般图像和人脸图像的性能。在多个数据集上的实验结果表明，该方法优于其他国家的最先进的方法，在主观和客观性能。
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.