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01-01-2020 | MATHEMATICAL THEORY OF IMAGES AND SIGNALS REPRESENTING, PROCESSING, ANALYSIS, RECOGNITION, AND UNDERSTANDING | Issue 1/2020

Pattern Recognition and Image Analysis 1/2020

Variance Based External Dictionary for Improved Single Image Super-Resolution

Journal:
Pattern Recognition and Image Analysis > Issue 1/2020
Authors:
Garima Pandey, Umesh Ghanekar
Important notes
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Garima Pandey completed her B.Tech. in electronics and communication engineering and M.Tech. in communication engineering. Presently doing PhD from National Institute of Technology Kurukshetra, Kurukshtera, Haryana, India. Active in field of Image Processing and Super-resolution.
https://static-content.springer.com/image/art%3A10.1134%2FS1054661820010101/MediaObjects/11493_2020_6054_Fig6_HTML.gif
Umesh Ghanekar completed his M.Tech. degree in Electronics andCommunication Engineering in 1988 from Indian Institute of Technology, Roorkee, India and PhD in computer engineering in 2013 from National Institute of Technology Kurukshetra, Kurukshtera, Haryana, India. Presently he is a Professor in the Department of Electronics and Communication Engineering at N.I.T. Kurukshetra. His research interests include signal and image processing.

Abstract

In this paper, we have proposed a novel method for image super-resolution using single image. Based on structural similarity index, an image similar to input low-resolution (LR) image is selected from the database and two separate external dictionaries i.e. smooth and textured, are formed from the selected image based on their variances. Different features are used for representation of different type of patches. For smooth patches norm luminance is used as feature vector and for textured patches it consist of first and second order gradients. In neighbor embedding process, a new parameter in combination with Euclidean distance has been introduced to eliminate outliers. Extensive simulations are performed to show superiority of the method.

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