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2013 (Vol. 6, Issue: 24)
Article Information:

A Visual Attention Model Based Image Fusion

Rishabh Gupta, M.R.Vimala Devi and M. Devi
Corresponding Author:  M. Devi 

Key words:  Focal connectivity multifocus, misregistration, pixel level fusion, tenengrade, wavelet transform, ,
Vol. 6 , (24): 4602-4606
Submitted Accepted Published
January 31, 2013 March 02, 2013 December 25, 2013

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.
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  Cite this Reference:
Rishabh Gupta, M.R.Vimala Devi and M. Devi, 2013. A Visual Attention Model Based Image Fusion.  Research Journal of Applied Sciences, Engineering and Technology, 6(24): 4602-4606.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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