Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154487
Title: From rank estimation to rank approximation : rank residual constraint for image restoration
Authors: Zha, Zhiyuan
Yuan, Xin
Wen, Bihan
Zhou, Jiantao
Zhang, Jiachao
Zhu, Ce
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Zha, Z., Yuan, X., Wen, B., Zhou, J., Zhang, J. & Zhu, C. (2019). From rank estimation to rank approximation : rank residual constraint for image restoration. IEEE Transactions On Image Processing, 29, 3254-3269. https://dx.doi.org/10.1109/TIP.2019.2958309
Project: SKL-IOTSC-2018-2020
077/2018/A2
022/2017/A1
Journal: IEEE Transactions on Image Processing 
Abstract: In this paper, we propose a novel approach for the rank minimization problem, termed rank residual constraint (RRC). Different from existing low-rank based approaches, such as the well-known nuclear norm minimization (NNM) and the weighted nuclear norm minimization (WNNM), which estimate the underlying low-rank matrix directly from the corrupted observation, we progressively approximate (approach) the underlying low-rank matrix via minimizing the rank residual. Through integrating the image nonlocal self-similarity (NSS) prior with the proposed RRC model, we apply it to image restoration tasks, including image denoising and image compression artifacts reduction. Toward this end, we first obtain a good reference of the original image groups by using the image NSS prior, and then the rank residual of the image groups between this reference and the degraded image is minimized to achieve a better estimate to the desired image. In this manner, both the reference and the estimated image in each iteration are improved gradually and jointly. Based on the group-based sparse representation model, we further provide a theoretical analysis on the feasibility of the proposed RRC model. Experimental results demonstrate that the proposed RRC model outperforms many state-of-the-art schemes in both the objective and perceptual qualities.
URI: https://hdl.handle.net/10356/154487
ISSN: 1057-7149
DOI: 10.1109/TIP.2019.2958309
Rights: © 2019 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

Page view(s)

20
Updated on May 24, 2022

Google ScholarTM

Check

Altmetric


Plumx

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