University of Oulu

Zha, Z., Zhang, X., Wu, Y., Wang, Q., Liu, X., Tang, L., & Yuan, X. (2018). Non-convex weighted ℓ nuclear norm based ADMM framework for image restoration. Neurocomputing, 311, 209–224.

Non-convex weighted ℓp nuclear norm based ADMM framework for image restoration

Saved in:
Author: Zha, Zhiyuan1; Zhang, Xinggan1; Wu, Yu1;
Organizations: 1School of Electronic Science and Engineering, Nanjing University
2The Center for Machine Vision and Signal Analysis, University of Oulu
3National Mobile Commun. Research Lab., Southeast University, Nanjing
4Nokia Bell Labs
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 4.6 MB)
Persistent link:
Language: English
Published: Elsevier, 2018
Publish Date: 2020-05-26


Inspired by the fact that the matrix formed by nonlocal similar patches in a natural image is of low rank, the nuclear norm minimization (NNM) has been widely used in various image processing studies. Nonetheless, nuclear norm based convex surrogate of the rank function usually over-shrinks the rank components since it treats different components equally, and thus may produce a result far from the optimum. To alleviate the aforementioned limitations of the nuclear norm, in this paper we propose a new method for image restoration via the non-convex weighted ℓp nuclear norm minimization (NCW-NNM), which is able to accurately impose the image structural sparsity and self-similarity simultaneously. To make the proposed model tractable and robust, the alternating direction method of multiplier (ADMM) framework is adopted to solve the associated non-convex minimization problem. Experimental results on various image restoration problems, including image deblurring, image inpainting and image compressive sensing (CS) recovery, demonstrate that the proposed method outperforms many current state-of-the-art methods.

see all

Series: Neurocomputing
ISSN: 0925-2312
ISSN-E: 1872-8286
ISSN-L: 0925-2312
Volume: 311
Pages: 209 - 224
DOI: 10.1016/j.neucom.2018.05.073
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work was supported by the NSFC (61571220, 61462052, 61502226 and 61601362).
Copyright information: © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license