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An Efficient Block based Feature Level Image Fusion Technique using Wavelet Transform and Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 52 - Number 12 |
| Year of Publication: 2012 |
| Authors: C. M. Sheela Rani, V. Vijaya Kumar, B. Sujatha |
10.5120/8253-1780
|
C. M. Sheela Rani, V. Vijaya Kumar, B. Sujatha . An Efficient Block based Feature Level Image Fusion Technique using Wavelet Transform and Neural Network. International Journal of Computer Applications. 52, 12 ( August 2012), 13-19. DOI=10.5120/8253-1780
Abstract
Image fusion is a process of combining relevant information from two or more images into a single informative image. In this paper, wavelet transform is integrated with neural network, which is one of the feature extraction or detection machine learning applications. This paper has derived an efficient block based feature level wavelet transform with neural network (BFWN) model for image fusion. In the proposed BFWN model, the two fusion techniques, discrete wavelet transform (DWT) and neural network (NN) are discussed for fusing IRS-1D images using LISS III scanner about the location Hyderabad, Vishakhapatnam, Mahaboobnagar and Patancheru in India. Also QuickBird image data and Landsat 7 image data are used to perform experiments on the proposed BFWN method. The features under study are contrast visibility, spatial frequency, energy of gradient, variance and edge information. Feed forward back propagation neural network is trained and tested for classification since the learning capability of neural network makes it feasible to customize the image fusion process. The trained neural network is then used to fuse the pair of source images. The proposed BFWN model is compared with DWT alone to assess the quality of the fused image. Experimental results clearly prove that the proposed BFWN model is an efficient and feasible algorithm for image fusion.
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