By Topic

Stationary image resolution enhancement on the basis of contourlet and wavelet transforms by means of the artificial neural network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Seyed Mohammad Entezarmahdi ; Mechanical Engineering Department, Shiraz University, Shiraz, Iran ; Mehran Yazdi

In this paper two transform based super resolution methods are presented for enhancing the resolution of a stationary image. In the first method, neural network is trained by wavelet transform coefficients of lower resolution of a given image, and then this neural network are used to estimate wavelet details subbands of that given image. In this way, by using these estimated subbands as wavelet details and the given image as the approximation image, a super-resolution image is made using the inverse wavelet transform. In the second proposed method, the wavelet transform is replaced by contourlet transform and the same mentioned procedure is applied. These two methods have been compared with each other and with the bicubic method on different types of images. The experimental results demonstrate the superiority performance of the proposed methods compared with regular stationary image resolution enhancing methods.

Published in:

2010 6th Iranian Conference on Machine Vision and Image Processing

Date of Conference:

27-28 Oct. 2010