Image Restoration Based on Adaptive Dual-Domain Filtering

Image restoration is a long-standing problem in low-level computer vision. In this paper, we offer a simple but effective estimation paradigm for various image restoration problems. Specifically, we first propose a model-based Gaussian denoising method Adaptive Dual-Domain Filtering (ADDF) by learni...

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Bibliographic Details
Published in:Mathematical problems in engineering 2018-01, Vol.2018 (2018), p.1-17
Main Authors: Zhu, Xiaolong, Chen, Huazhu, Zhao, Chenping, Feng, Xiang-Chu, He, Ruiqiang, Xu, Chen
Format: Article
Language:English
Subjects:
Adaptive filters
Algorithms
Clustering
Coining
Computer vision
Decomposition
Filtration
Fourier transforms
Image filters
Image resolution
Image restoration
Machine learning
Morphology
Noise
Noise reduction
Optimization
Principal components analysis
Random noise
Signal processing
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Summary:Image restoration is a long-standing problem in low-level computer vision. In this paper, we offer a simple but effective estimation paradigm for various image restoration problems. Specifically, we first propose a model-based Gaussian denoising method Adaptive Dual-Domain Filtering (ADDF) by learning the optimal confidence factors which are adjusted adaptively with Gaussian noise standard deviation. In addition, by generalizing this learning approach to Laplace noise, the learning algorithm of the optimum confidence factors in Laplace denoising is presented. Finally, the proposed ADDF is tactfully plugged into the method frameworks of off-the-shelf image deblurring and single image super-resolution (SISR). The approach, coining the name Plug-ADDF, achieves promising performance. Extensive experiments validate that the proposed ADDF for Gaussian and Laplace noise removals indeed results in visual and quantitative improvements over some existing state-of-the-art methods. Moreover, our Plug-ADDF for image deblurring and SISR also demonstrates superior performance objectively and subjectively.
ISSN:1024-123X
1563-5147
DOI:10.1155/2018/4790174