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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Veröffentlicht in:IET image processing Jg. 9; H. 5; S. 347 - 357
Hauptverfasser: Liu, Yu, Wang, Zengfu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 01.05.2015
Schlagworte:
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
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
ISSN:1751-9659, 1751-9667
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1751-9659
1751-9667
DOI:10.1049/iet-ipr.2014.0311