Title: Image super-resolution algorithm's research using convolutional sparse coding model

Authors: Bin Wang; Jun Deng; Yanjing Sun

Addresses: Department of IoT Engineering, Xi'an University of Science and Technology, Xi'an 710048, China ' Department of Safety Science Engineering, Xi'an University of Science and Technology, Xi'an 710048, China ' Department of IoT Engineering, Xi'an University of Science and Technology, Xi'an 710048, China; Department of Communication Engineering, China University of Mining and Technology, Xuzhou 221116, China

Abstract: According to image super-resolution reconstruction for convolutional sparse coding model, a novel super-resolution reconstruction algorithm named four-channel convolutional sparse coding model has proposed via improving convolutional sparse coding method. In the proposed method, the testing image was put in four-channel via rotating image ninety degrees in four times. Then, the high-frequent part and low-frequent part were reconstructed by means of convolutional sparse coding method and cubic interpolation method respectively. Finally, the reconstructed high-resolution image has obtained via the process of weighting on four images. The proposed method not only overcomes the problem of consistency for the overlapping patches, but also improves the detail contour for the reconstructed image and enhances its stability. The experimental results have shown that the proposed method has better PSNR, SSIM, and noise immunity than some classical super-resolution reconstruction methods.

Keywords: image reconstruction; super-resolution; convolutional sparse coding; CSC; stability of proposed algorithm.

DOI: 10.1504/IJICT.2019.102057

International Journal of Information and Communication Technology, 2019 Vol.15 No.1, pp.92 - 106

Received: 14 Feb 2018
Accepted: 21 Mar 2018

Published online: 03 Sep 2019 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article