NDLI: Super resolution reconstruction based on block matching and three-dimensional filtering with sharpening

Content Provider	IET Digital Library
Author	Kim, Yookyung Oh, Han Bilgin, Ali
Abstract	Super resolution (SR) reconstruction is often considered to be an inverse problem in the sense that unknown high resolution images are sought for giving low resolution images. Recent studies have shown that the sparsity regularisation used in compressed sensing (CS) reconstruction improves the performance of SR reconstruction. Furthermore, under the assumption that mutually similar regions exist within a natural image, non-local (NL) estimation produces accurate estimates for given degraded images. The incorporation of this NL estimation in SR reconstruction has been shown to yield better reconstructions. In this study, the authors propose the use of block matching and three-dimensional filtering with sharpening estimation as the regularisation constraint under the CS-based SR framework. This estimation collects similar blocks and adaptively filters them by the shrinkage of the transform coefficients. It recovers detailed structures while attenuating ringing artefacts. In addition, a sharpening technique used in the estimation also emphasises edges. As a result, the proposed SR algorithm searches for the solution that is similar to this enhanced estimate from among all feasible solutions. The experimental results demonstrate that the proposed method provides high-quality SR images, both numerically and subjectively.
Starting Page	1048
Ending Page	1056
Page Count	9
ISSN	17519659
Volume Number	9
e-ISSN	17519667
Issue Number	Issue 12, Dec (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/9/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2014.0566
Journal	IET Image Processing
Publisher Date	2015-12-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	3D Filtering Adaptive Filter Block Matching Compressed Sensing Compressed Sensing Reconstruction Computer Vision And Image Processing Technique Estimation Theory Filtering Method in Signal Processing High Resolution Image Image And Video Coding Image Filtering Image Matching Image Recognition Image Reconstruction Image Resolution Inverse Problem Low Resolution Image Mathematical Analysis Natural Image Nonlocal Estimation Sharpening Estimation Sparsity Regularisation Statistics Stereo Image Processing Super Resolution Reconstruction
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

Compressive sensing via reweighted TV and nonlocal sparsity regularisation

Compressed sensing magnetic resonance imaging based on dictionary updating and block-matching and three-dimensional filtering regularisation

Image compressive sensing reconstruction based on collaboration reduced rank preprocessing

Decoupled Algorithm for MRI Reconstruction Using Nonlocal Block Matching Model: BM3D-MRI

Texture and edge preserving multiframe super-resolution

Video denoising algorithm based on improved dual-domain filtering and 3D block matching

Sparsity-aware adaptive block-based compressive sensing

Guided filter-based multi-scale super-resolution reconstruction

Interweaving Permutation Meets Block Compressed Sensing

Super resolution reconstruction based on block matching and three-dimensional filtering with sharpening

Similar Documents

Compressive sensing via reweighted TV and nonlocal sparsity regularisation

Compressed sensing magnetic resonance imaging based on dictionary updating and block-matching and three-dimensional filtering regularisation

Image compressive sensing reconstruction based on collaboration reduced rank preprocessing

Decoupled Algorithm for MRI Reconstruction Using Nonlocal Block Matching Model: BM3D-MRI

Texture and edge preserving multiframe super-resolution

Video denoising algorithm based on improved dual-domain filtering and 3D block matching

Sparsity-aware adaptive block-based compressive sensing

Guided filter-based multi-scale super-resolution reconstruction

Interweaving Permutation Meets Block Compressed Sensing

Super resolution reconstruction based on block matching and three-dimensional filtering with sharpening