NDLI: Image Denoising Via Sparse Dictionaries Constructed by Subspace Learning

Content Provider	Springer Nature Link
Author	Kuang, Yin Zhang, Lei Yi, Zhang
Copyright Year	2014
Abstract	In this paper, we propose a combinational algorithm for the removal of zero-mean white and homogeneous Gaussian additive noise from a given image. Image denoising is formulated as an optimization problem. This is iteratively solved by a weighted basis pursuit (BP) in the closed affine subspace. The patches extracted from a given noisy image can be sparsely and approximately represented by adaptively choosing a few nearest neighbors. The approximate reconstruction of these denoised patches is performed by the sparse representation on two dictionaries, which are built by a discrete cosine transform and the noisy patches, respectively. Experiments show that the proposed algorithm outperforms both BP denoising and Sparse K-SVD. This is because the underlying structure of natural images is better captured and preserved. The results are comparable to those of the block-matching 3D filtering algorithm.
Starting Page	2151
Ending Page	2171
Page Count	21
File Format	PDF
ISSN	0278081X
Journal	Circuits, Systems, and Signal Processing
Volume Number	33
Issue Number	7
e-ISSN	15315878
Language	English
Publisher	Springer US
Publisher Date	2014-01-23
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Image denoising Optimization problem Weighted BPDN K-SVD Sparse K-SVD Closed affine subspace learning Circuits and Systems Electrical Engineering Signal, Image and Speech Processing Electronics and Microelectronics, Instrumentation
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Signal Processing

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

Single-Pass K-SVD for Efficient Dictionary Learning

Clustering-Based Image Sparse Denoising in Wireless Multimedia Sensor Networks

Learning dictionary via subspace segmentation for sparse representation

Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation

Image Denoising via Sparse Representation Over Grouped Dictionaries With Adaptive Atom Size

K-SVD with a real L0 optimization: application to image denoising

A Particle Swarm Optimization Technique-Based Parametric Wavelet Thresholding Function for Signal Denoising

Enhancing Image Denoising by Controlling Noise Incursion in Learned Dictionaries

Neighboring Coefficients Preservation for Signal Denoising

Image Denoising Via Sparse Dictionaries Constructed by Subspace Learning

Similar Documents

Single-Pass K-SVD for Efficient Dictionary Learning

Clustering-Based Image Sparse Denoising in Wireless Multimedia Sensor Networks

Learning dictionary via subspace segmentation for sparse representation

Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation

Image Denoising via Sparse Representation Over Grouped Dictionaries With Adaptive Atom Size

K-SVD with a real L0 optimization: application to image denoising

A Particle Swarm Optimization Technique-Based Parametric Wavelet Thresholding Function for Signal Denoising

Enhancing Image Denoising by Controlling Noise Incursion in Learned Dictionaries

Neighboring Coefficients Preservation for Signal Denoising

Image Denoising Via Sparse Dictionaries Constructed by Subspace Learning