Multi-Channel and Multi-Model-Based Autoencoding Prior for Grayscale Image Restoration

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...

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Vydáno v:IEEE transactions on image processing Ročník 29; s. 142 - 156
Hlavní autoři: Li, Sanqian, Qin, Binjie, Xiao, Jing, Liu, Qiegen, Wang, Yuhao, Liang, Dong
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
auxiliary variable technique
channel prior
denoising autoencoder network
Gray-scale
Grayscale image restoration
Image denoising
Image processing
Image quality
Image restoration
Iterative methods
Minimization
multi-model
Noise reduction
proximal gradient descent
Task analysis
Training
ISSN:1057-7149, 1941-0042, 1941-0042
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Shrnutí: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.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2019.2931240