Paper Info
Title
Multi-channel and Multi-model based Autoencoding Prior for Grayscale Image Restoration.
Abstract
Image restoration (IR) is a long-standing challenging problem in low-level image processing. It is of utmost importance to learn good image priors for pursuing visually pleasing results. In this paper, we develop a multi-channel and multi-model-based denoising autoencoder network as image prior for solving IR problem. Specifically, the network that trained on RGB-channel images is used to construct a prior at first, and then the learned prior is incorporated into single-channel grayscale IR tasks. To achieve the goal, we employ the auxiliary variable technique to integrate the higher-dimensional network-driven prior information into the iterative restoration procedure. In addition, according to the weighted aggregation idea, a multi-model strategy is put forward to enhance the network stability that favors to avoid getting trapped in local optima. Extensive experiments on image deblurring and deblocking tasks show that the proposed algorithm is efficient, robust, and yields state-of-the-art restoration quality on grayscale images.
Year
DOI
Venue
2020
10.1109/TIP.2019.2931240
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Keywords
Field
DocType
Image restoration,Gray-scale,Task analysis,Noise reduction,Image denoising,Training,Minimization
Computer vision,Deblurring,Pattern recognition,Local optimum,Image processing,Minification,Artificial intelligence,Image restoration,Prior probability,Mathematics,Deblocking filter,Grayscale
Journal
Volume
Issue
ISSN
29
1
1057-7149
Citations 
PageRank 
References 
4
0.38
26
Authors
6
Name
Order
Citations
PageRank
Sanqian Li171.76
Binjie Qin2507.85
Jing Xiao34212.17
Qiegen Liu424928.53
Yuhao Wang517038.41
Dong Liang64710.50