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Performance Analysis of Wavelet Transforms for Learning based Single frame Image Super-resolution

by Anil A. Patil, Jyoti Singhai
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 38 - Number 3
Year of Publication: 2012
Authors: Anil A. Patil, Jyoti Singhai
10.5120/4666-6770

Anil A. Patil, Jyoti Singhai . Performance Analysis of Wavelet Transforms for Learning based Single frame Image Super-resolution. International Journal of Computer Applications. 38, 3 ( January 2012), 9-14. DOI=10.5120/4666-6770

@article{ 10.5120/4666-6770,
author = { Anil A. Patil, Jyoti Singhai },
title = { Performance Analysis of Wavelet Transforms for Learning based Single frame Image Super-resolution },
journal = { International Journal of Computer Applications },
issue_date = { January 2012 },
volume = { 38 },
number = { 3 },
month = { January },
year = { 2012 },
issn = { 0975-8887 },
pages = { 9-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume38/number3/4666-6770/ },
doi = { 10.5120/4666-6770 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:24:34.859363+05:30
%A Anil A. Patil
%A Jyoti Singhai
%T Performance Analysis of Wavelet Transforms for Learning based Single frame Image Super-resolution
%J International Journal of Computer Applications
%@ 0975-8887
%V 38
%N 3
%P 9-14
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image super resolution concept has been introduced for image enhancement in various applications. Image enhancement is crucial operation essential for reducing different possible degradations of the captured image. More sophisticated techniques are already proposed. Wavelet transform based algorithms are widely used in many applications. Wavelet transform/s is used to extrapolate missing high frequency components which improve the efficiency of an algorithm. In this paper for super resolving the images, wavelet coefficients of the unknown high resolution image are learnt from a set of high resolution training images in wavelet domain. The performance of different discrete orthogonal and a biorthogonal wavelets have been evaluated on different class of images in terms of MSE and PSNR. The outcome of this work suggests that use of db4 wavelet transform is appropriate for super resolution technique. The PSNR obtained with this transform outfits for other wavelet transforms.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Super-resolution(SR) Wavelet Transform Learning Method

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