Research Article | OPEN ACCESS
A Visual Attention Model Based Image Fusion
1Rishabh Gupta, 2M.R.Vimala Devi and 2M. Devi
1School of Electrical Sciences, VIT University, Vellore
2Department of ECE, SASTRA University, Thanjavur, Tamilnadu 613401, India
Research Journal of Applied Sciences, Engineering and Technology 2013 24:4602-4606
Received: January 31, 2013 | Accepted: March 02, 2013 | Published: December 25, 2013
Abstract
To develop an efficient image fusion algorithm based on visual attention model for images with distinct objects. Image fusion is a process of combining complementary information from multiple images of the same scene into an image, so that the resultant image contains a more accurate description of the scene than any of the individual source images. The two basic fusion techniques are pixel level and region level fusion. Pixel level fusion deals with the operations on each and every pixel separately. The various pixel level techniques are averaging, stationary wavelet transforms, discrete wavelet transforms, Principal Component Analysis (PCA). But because of less sensitivity to noise and mis-registration, the region level image fusion is an emerging approach in the field of multifocus image fusion. The most appreciated approaches in region-based methods are multifocus image fusion using the concept of focal connectivity and spatial frequency. These two methods works well on still images as well as on video frames as inputs. A new region based technique is been proposed for the multifocus images having distinct objects. The method is based on the visual attention models and results obtained are appreciating for the distinct objects input images. The Proposed method results are highlighted using tenengrade and extended spatial frequency as performance parameters by taking several pairs of multi-focus input images like microscopic images, forensic images and video frames.
Keywords:
Focal connectivity multifocus, misregistration, pixel level fusion, tenengrade, wavelet transform,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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