NDLI: Simultaneous image fusion and denoising with adaptive sparse representation

Content Provider	IET Digital Library
Author	Liu, Yu Wang, Zengfu
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.
Starting Page	347
Ending Page	357
Page Count	11
ISSN	17519659
Volume Number	9
e-ISSN	17519667
Issue Number	Issue 5, May (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/9/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2014.0311
Journal	IET Image Processing
Publisher Date	2014-10-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Sparse Representation ASR Model Compact Sub-dictionaries Computer Vision And Image Processing Technique Gradient Information High Computational Cost High-quality Image Patches Image Classification Image Denoising Image Fusion Image Processing Image Recognition Image Reconstruction Image Representation Knowledge Engineering Technique Learning in AI Multifocus Image Sets Multimodal Image Sets Objective Assessment Potential Visual Artefacts Sensor Fusion Signal Modelling Technique Signal Reconstruction Requirement Simultaneous Image Fusion Single Redundant Dictionary Learning Source Image Patches Visual Quality
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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