Single-image super-resolution using kernel recursive least squares. Issue 8 (November 2016)

• Record Type: Journal Article
• Authors: Anver, Jesna; Abdulla, P.
• Is Part Of: Signal, image and video processing. Volume 10:Issue 8(2016)
• Page Start: 1551
• Page End: 1558
• Publication Date: 2016-11
• Subjects: Super-resolution -- Approximate linear dependence kernel recursive least square -- Sliding window kernel recursive least square -- Kernel k-means; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-016-0970-x
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract Online single-image super-resolution of an image has been obtained here. The high-resolution image is constructed from a dictionary of features that approximately spans the subspace of regression. This paper classifies the low-resolution image using the kernelk -means clustering algorithm and makes an extensive study using the approximate linear dependence kernel recursive least square and sliding window kernel recursive least squares for super-resolving the image from the existing low- and high-resolution images. The super-resolution using kernel recursive least square significantly provides an improvement up on the support vector regression solution, both in terms of speed, dictionary samples and also gives a better PSNR value.