Synthetic image super resolution using FeatureMatch

Ramakanth, Avinash S and Babu, Venkatesh R (2015) Synthetic image super resolution using FeatureMatch. In: MULTIMEDIA TOOLS AND APPLICATIONS, 74 (17). pp. 6691-6707.

PDF Mul_Too_and_App_74-17_ 6691_2015.pdf - Published Version Restricted to Registered users only Download (1MB) | Request a copy

Abstract

In this paper, we propose a super resolution (SR) method for synthetic images using FeatureMatch. Existing state-of-the-art super resolution methods are learning based methods, where a pair of low-resolution and high-resolution dictionary pair are trained, and this trained pair is used to replace patches in low-resolution image with appropriate matching patches from the high-resolution dictionary. In this paper, we show that by using Approximate Nearest Neighbour Fields (ANNF), and a common source image, we can by-pass the learning phase, and use a single image for dictionary. Thus, reducing the dictionary from a collection obtained from hundreds of training images, to a single image. We show that by modifying the latest developments in ANNF computation, to suit super resolution, we can perform much faster and more accurate SR than existing techniques. To establish this claim we will compare our algorithm against various state-of-the-art algorithms, and show that we are able to achieve better and faster reconstruction without any training phase.

Item Type:	Journal Article
Publication:	MULTIMEDIA TOOLS AND APPLICATIONS
Publisher:	SPRINGER
Additional Information:	Copy right for this article belongs to the SPRINGER, VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
Keywords:	Super resolution; PatchMatch; Synthetic images; Approximate nearest-neighbour field
Department/Centre:	Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited:	24 Sep 2015 05:07
Last Modified:	24 Sep 2015 05:07
URI:	http://eprints.iisc.ac.in/id/eprint/52393

Actions (login required)

View Item