Simultaneous image fusion and denoising with adaptive sparse representation
In this study, a novel adaptive sparse representation (ASR) model is presented for simultaneous image fusion and denoising. As a powerful signal modelling technique, sparse representation (SR) has been successfully employed in many image processing applications such as denoising and fusion. In tradi...
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| Published in | IET image processing Vol. 9; no. 5; pp. 347 - 357 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
The Institution of Engineering and Technology
01.05.2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1751-9659 1751-9667 |
| DOI | 10.1049/iet-ipr.2014.0311 |
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| Abstract | In this study, a novel adaptive sparse representation (ASR) model is presented for simultaneous image fusion and denoising. As a powerful signal modelling technique, sparse representation (SR) has been successfully employed in many image processing applications such as denoising and fusion. In traditional SR-based applications, a highly redundant dictionary is always needed to satisfy signal reconstruction requirement since the structures vary significantly across different image patches. However, it may result in potential visual artefacts as well as high computational cost. In the proposed ASR model, instead of learning a single redundant dictionary, a set of more compact sub-dictionaries are learned from numerous high-quality image patches which have been pre-classified into several corresponding categories based on their gradient information. At the fusion and denoising processes, one of the sub-dictionaries is adaptively selected for a given set of source image patches. Experimental results on multi-focus and multi-modal image sets demonstrate that the ASR-based fusion method can outperform the conventional SR-based method in terms of both visual quality and objective assessment. |
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| AbstractList | In this study, a novel adaptive sparse representation (ASR) model is presented for simultaneous image fusion and denoising. As a powerful signal modelling technique, sparse representation (SR) has been successfully employed in many image processing applications such as denoising and fusion. In traditional SR-based applications, a highly redundant dictionary is always needed to satisfy signal reconstruction requirement since the structures vary significantly across different image patches. However, it may result in potential visual artefacts as well as high computational cost. In the proposed ASR model, instead of learning a single redundant dictionary, a set of more compact sub-dictionaries are learned from numerous high-quality image patches which have been pre-classified into several corresponding categories based on their gradient information. At the fusion and denoising processes, one of the sub-dictionaries is adaptively selected for a given set of source image patches. Experimental results on multi-focus and multi-modal image sets demonstrate that the ASR-based fusion method can outperform the conventional SR-based method in terms of both visual quality and objective assessment. |
| Author | Wang, Zengfu Liu, Yu |
| Author_xml | – sequence: 1 givenname: Yu surname: Liu fullname: Liu, Yu email: liuyu1@mail.ustc.edu.cn organization: 1Department of Automation, University of Science and Technology of China, Hefei 230026, People's Republic of China – sequence: 2 givenname: Zengfu surname: Wang fullname: Wang, Zengfu organization: 2Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, People's Republic of China |
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| Keywords | image processing multifocus image sets image classification image fusion gradient information ASR model single redundant dictionary learning simultaneous image fusion adaptive sparse representation objective assessment image reconstruction visual quality potential visual artefacts image denoising high computational cost source image patches compact sub-dictionaries high-quality image patches multimodal image sets signal modelling technique image representation learning (artificial intelligence) signal reconstruction requirement |
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| SubjectTerms | adaptive sparse representation ASR model compact sub‐dictionaries Computer vision gradient information high computational cost high‐quality image patches image classification image denoising image fusion Image processing image reconstruction image representation learning (artificial intelligence) multifocus image sets multimodal image sets Noise reduction objective assessment potential visual artefacts Redundant Representations signal modelling technique signal reconstruction requirement simultaneous image fusion single redundant dictionary learning source image patches Strontium Visual visual quality |
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| Title | Simultaneous image fusion and denoising with adaptive sparse representation |
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