Super-resolution reconstruction for a single image based on self-similarity and compressed sensing. Issue 3 (September 2018)

• Record Type: Journal Article
• Authors: Yang, Qiang; Wang, Huajun
• Is Part Of: Journal of algorithms & computational technology. Volume 12:Issue 3(2018)
• Page Start: 234
• Page End: 244
• Publication Date: 2018-09
• Subjects: Image similarity -- learning dictionary -- compressed sensing -- super-resolution reconstruction; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748301818778244
• Languages: English
• ISSNs: 1748-3018

Abstract

Super-resolution image reconstruction can achieve favorable feature extraction and image analysis. This study first investigated the image's self-similarity and constructed high-resolution and low-resolution learning dictionaries; then, based on sparse representation and reconstruction algorithm in compressed sensing theory, super-resolution reconstruction (SRSR) of a single image was realized. The proposed algorithm adopted improved K-SVD algorithm for sample training and learning dictionary construction; additionally, the matching pursuit algorithm was improved for achieving single-image SRSR based on image's self-similarity and compressed sensing. The experimental results reveal that the proposed reconstruction algorithm shows better visual effect and image quality than the degraded low-resolution image; moreover, compared with the reconstructed images using bilinear interpolation and sparse-representation-based algorithms, the reconstructed image using the proposed algorithm has a higher PSNR value and thus exhibits more favorable super-resolution image reconstruction performance.