NDLI: Omni-gradient-based total variation minimisation for sparse reconstruction of omni-directional image

Content Provider	IET Digital Library
Author	Lou, Jingtao Li, Yongle Liu, Yu Tan, Shuren Zhang, Maojun
Abstract	Total variation (TV) minimisation algorithms have been successfully applied in compressive sensing (CS) recovery for natural images owing to its advantage of preserving edges. However, traditional TV is no longer appropriate for omni-directional image processing because of the distortions in catadioptric imaging systems. The omni-gradient computing method combined with the characteristics of omni-directional imaging is proposed in this study. To reconstruct the image from its compressive samples, the omni-total variation (omni-TV) regularisation based on omni-gradient is utilised instead of traditional TV during the image restoration. The experimental results show that the omni-directional images can be reconstructed effectively and accurately. Compared with the classical TV minimisation model, the images recovered based on omni-TV model can provide higher quality both in subjective evaluation and objective evaluation.
Starting Page	397
Ending Page	405
Page Count	9
ISSN	17519659
Volume Number	8
e-ISSN	17519667
Issue Number	Issue 7, Jul (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/8/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2013.0330
Journal	IET Image Processing
Publisher Date	2013-11-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Catadioptric Imaging System Compressed Sensing Compressive Sensing Recovery Computer Vision And Image Processing Technique CS Recovery Edges Preservation Gradient Method Image Reconstruction Image Restoration Minimisation Natural Image Objective Evaluation Omni-gradient Computing Method Omni-gradient-based Total Variation Minimisation Omni-TV Regularisation Omnidirectional Image Processing Optical, Image And Video Signal Processing Optimisation Technique Sparse Reconstruction Subjective Evaluation
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

Gradient-based compressive sensing for noise image and video reconstruction

Iterative Directional Total Variation Refinement for Compressive Sensing Image Reconstruction

Colour compressed sensing imaging via sparse difference and fractal minimisation recovery

Gradient-based algorithm for designing sensing matrix considering real mutual coherence for compressed sensing systems

Stroke width-based directional total variation regularisation for document image super resolution

High-resolution ISAR imaging via MMV-based block-sparse signal recovery

Simple algorithm for L1-norm regularisation-based compressed sensing and image restoration

Joint Bayesian and Greedy Recovery for Compressive Sensing

Omni-gradient-based total variation minimisation for sparse reconstruction of omni-directional image

Similar Documents

Compressive sensing via reweighted TV and nonlocal sparsity regularisation

Gradient-based compressive sensing for noise image and video reconstruction

Iterative Directional Total Variation Refinement for Compressive Sensing Image Reconstruction

Colour compressed sensing imaging via sparse difference and fractal minimisation recovery

Gradient-based algorithm for designing sensing matrix considering real mutual coherence for compressed sensing systems

Stroke width-based directional total variation regularisation for document image super resolution

High-resolution ISAR imaging via MMV-based block-sparse signal recovery

Simple algorithm for L1-norm regularisation-based compressed sensing and image restoration

Joint Bayesian and Greedy Recovery for Compressive Sensing

Omni-gradient-based total variation minimisation for sparse reconstruction of omni-directional image