Please use this identifier to cite or link to this item:
Title: Image recovery via transform learning and low-rank modeling: the power of complementary regularizers
Authors: Wen, Bihan 
Li, Yanjun
Bresler, Yoram
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Wen, B., Li, Y. & Bresler, Y. (2020). Image recovery via transform learning and low-rank modeling: the power of complementary regularizers. IEEE Transactions On Image Processing, 29, 5310-5323.
Journal: IEEE Transactions on Image Processing
Abstract: Recent works on adaptive sparse and on low-rank signal modeling have demonstrated their usefulness in various image/video processing applications. Patch-based methods exploit local patch sparsity, whereas other works apply low-rankness of grouped patches to exploit image non-local structures. However, using either approach alone usually limits performance in image reconstruction or recovery applications. In this work, we propose a simultaneous sparsity and low-rank model, dubbed STROLLR, to better represent natural images. In order to fully utilize both the local and non-local image properties, we develop an image restoration framework using a transform learning scheme with joint low-rank regularization. The approach owes some of its computational efficiency and good performance to the use of transform learning for adaptive sparse representation rather than the popular synthesis dictionary learning algorithms, which involve approximation of NP-hard sparse coding and expensive learning steps. We demonstrate the proposed framework in various applications to image denoising, inpainting, and compressed sensing based magnetic resonance imaging. Results show promising performance compared to state-of-the-art competing methods.
ISSN: 1057-7149
DOI: 10.1109/TIP.2020.2980753
Schools: School of Electrical and Electronic Engineering 
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

Citations 10

Updated on Dec 4, 2023

Web of ScienceTM
Citations 10

Updated on Oct 26, 2023

Page view(s)

Updated on Nov 30, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.