Single image super-resolution with non-local balanced low-rank matrix restoration

Xinge You, Weiyong Xue, Jiajia Lei, Peng Zhang, Yiu Ming CHEUNG, Yuanyan Tang, Naiding Zhou

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

12 Citations (Scopus)


Single image super-resolution (SR) has gained popularity to construct a high-resolution (HR) image from a single low-resolution (LR) version. More recently, non-local self similarity (NSS) has been attracted enormous interests in the field of SR, and the non-local means (NLM)-based methods are classical NSS-based SR methods. However, NLM-based methods neglect the structure information in the patches and structural similarity between patches, so it will be prone to introduce unexpected details into resultant HR images. In this paper, we propose a non-local balanced low rank matrix restoration model (NB-LRM) to improve the performance of SR which will overcome the drawbacks of NLM-based methods and take full advantage of the NSS prior. The proposed algorithm formulates the constrained optimization problem for HR image recovery. First, to take advantage of the local structure in the patch and the structural similarity between the non-local similar patches, we propose a measurement of the similarity based on both Euclidean distance and Pearson distance, then reconstruct the target patch by weighted average the similar patches. Second, to guarantee the structural similarity and linear correlation between the target patch and similar patches, we propose a new low rank regular term. Third, we introduce the iterative low rank regular algorithm to solve our model. Addition, this method doesn't need other image priors and can produce more robust reconstruction of image local structures. Compared with state-of-the-art SR methods, the proposed NB-LRM method achieves highly competitive PSNR and SSIM result, while demonstrating better edge and texture preservation performance.

Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition, ICPR 2016
Number of pages6
ISBN (Electronic)9781509048472
Publication statusPublished - 1 Jan 2016
Event23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico
Duration: 4 Dec 20168 Dec 2016

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference23rd International Conference on Pattern Recognition, ICPR 2016

Scopus Subject Areas

  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Single image super-resolution with non-local balanced low-rank matrix restoration'. Together they form a unique fingerprint.

Cite this