NDLI: Robust image fusion with block sparse representation and online dictionary learning

Content Provider	IET Digital Library
Author	Xiang, Fengtao Jian, Zhang Liang, Pan Xueqiang, Gu
Abstract	For many image fusion problems, the most used technique is selecting features with rich information. The robust image fusion method based on block compressive sensing principle is studied here. Compressive sensing is known to provide an effective method with high accuracy. The framework of the proposed method is given in various perspectives: block sparse representations, restoration algorithms, feature extraction, online dictionary learning, and fusion rules. In terms of restoration of fused images, the split Bregman iteration is adopted. The proposed method can acquire well fusion image from source images and remove some degradations simultaneously, such as noises and blurring effect. In addition, both ‘maximum selection’ and ‘weighted mean’ are investigated as fusion rules, which can preserve more information. Generally, the proposed method can achieve better fusion result from the source images. The experiments with or without noise source images both illustrate that the proposed method has relatively comparative fusion results.
Starting Page	345
Ending Page	353
Page Count	9
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 3, Mar (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0327
Journal	IET Image Processing
Publisher Date	2017-09-05
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Block Compressive Sensing Principle Block Sparse Representation Computer Vision And Image Processing Technique Feature Extraction Feature Selection Fused Image Restoration Fusion Rule Image Denoising Image Fusion Image Recognition Image Representation Image Restoration Interpolation And Function Approximation Iterative Method Maximum Selection Numerical Analysis Online Dictionary Learning Restoration Algorithm Robust Image Fusion Method Source Image Split Bregman Iteration Weighted Mean
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Multi-focus image fusion via morphological similarity-based dictionary construction and sparse representation

Reduced quaternion matrix-based sparse representation and its application to colour image processing

Generalised non-locally centralised image de-noising using sparse dictionary

Image decomposition-based blind image deconvolution model by employing sparse representation

Robust multi-feature visual tracking via multi-task kernel-based sparse learning

Decision fusion using virtual dictionary-based sparse representation for robust SAR automatic target recognition

Image fusion method based on simultaneous sparse representation with non-subsampled contourlet transform

Robust visual tracking via two-stage binocular sparse learning

Group-Sparse Representation With Dictionary Learning for Medical Image Denoising and Fusion

Robust image fusion with block sparse representation and online dictionary learning

Similar Documents

Multi-focus image fusion via morphological similarity-based dictionary construction and sparse representation

Reduced quaternion matrix-based sparse representation and its application to colour image processing

Generalised non-locally centralised image de-noising using sparse dictionary

Image decomposition-based blind image deconvolution model by employing sparse representation

Robust multi-feature visual tracking via multi-task kernel-based sparse learning

Decision fusion using virtual dictionary-based sparse representation for robust SAR automatic target recognition

Image fusion method based on simultaneous sparse representation with non-subsampled contourlet transform

Robust visual tracking via two-stage binocular sparse learning

Group-Sparse Representation With Dictionary Learning for Medical Image Denoising and Fusion

Robust image fusion with block sparse representation and online dictionary learning