NDLI: Blind Separation of Image Sources via Adaptive Dictionary Learning

Content Provider	IEEE Xplore Digital Library
Author	Abolghasemi, V. Ferdowsi, S. Sanei, S.
Copyright Year	1992
Abstract	Sparsity has been shown to be very useful in source separation of multichannel observations. However, in most cases, the sources of interest are not sparse in their current domain and one needs to sparsify them using a known transform or dictionary. If such a priori about the underlying sparse domain of the sources is not available, then the current algorithms will fail to successfully recover the sources. In this paper, we address this problem and attempt to give a solution via fusing the dictionary learning into the source separation. We first define a cost function based on this idea and propose an extension of the denoising method in the work of Elad and Aharon to minimize it. Due to impracticality of such direct extension, we then propose a feasible approach. In the proposed hierarchical method, a local dictionary is adaptively learned for each source along with separation. This process improves the quality of source separation even in noisy situations. In another part of this paper, we explore the possibility of adding global priors to the proposed method. The results of our experiments are promising and confirm the strength of the proposed approach.
Sponsorship	IEEE Signal Processing Society
Page Count	10
File Size	4529047
Starting Page	2921
Ending Page	2930
File Format	PDF
ISSN	10577149
Volume Number	21
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Source separation Noise Noise reduction Hidden Markov models Estimation Vectors sparsity Blind source separation (BSS) dictionary learning image denoising morphological component analysis (MCA)
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design 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

Hyperspectral BSS Using GMCA With Spatio-Spectral Sparsity Constraints

Hidden Markov models for wavelet-based blind source separation

Sparsity-Based Poisson Denoising With Dictionary Learning

Sparsity and Morphological Diversity in Blind Source Separation

Multiplicative Noise Removal via a Learned Dictionary

Clustering-Based Denoising With Locally Learned Dictionaries

Automatic Single-Image-Based Rain Streaks Removal via Image Decomposition

Maximum Likelihood Blind Image Separation Using Nonsymmetrical Half-Plane Markov Random Fields

Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

Blind Separation of Image Sources via Adaptive Dictionary Learning

Similar Documents

Hyperspectral BSS Using GMCA With Spatio-Spectral Sparsity Constraints

Hidden Markov models for wavelet-based blind source separation

Sparsity-Based Poisson Denoising With Dictionary Learning

Sparsity and Morphological Diversity in Blind Source Separation

Multiplicative Noise Removal via a Learned Dictionary

Clustering-Based Denoising With Locally Learned Dictionaries

Automatic Single-Image-Based Rain Streaks Removal via Image Decomposition

Maximum Likelihood Blind Image Separation Using Nonsymmetrical Half-Plane Markov Random Fields

Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

Blind Separation of Image Sources via Adaptive Dictionary Learning