Improved Block Based Feature Level Image Fusion Technique using Multiwavelet with Neural Network
C. M. Sheela Rani1, V. VijayaKumar2, B. V. Ramana Reddy3
1C. M. Sheela Rani, Research Scholar, Dept of Computer Science & Engg, Acharya Nagarjuna University, Guntur, India.
2V. VijayaKumar, Dept of Computers, Head SSRF, GIET, JNTUK, Rajahmundry, A.P, India.
3B. V. Ramana Reddy, Assoc. Prof in CSE, JNTUH, Hyderabad, Andhra Pradesh, India
Manuscript received on September 01, 2012. | Revised Manuscript received on September 02, 2012. | Manuscript published on September 05, 2012. | PP: 265-271 | Volume-2 Issue-4, September 2012. | Retrieval Number: D0963082412/2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Image fusion is defined as the process of combining two or more different images into a new single image retaining the important features from each image with extended information content. To overcome the spectral distortions in the fused image, many multiresolution based approaches have been proposed. They include different pyramid transforms and discrete scalar wavelet transforms. The multiwavelet transform (MWT) produces a non-redundant image representation, which provides better spatial and spectral localization of image formation. This paper has derived an efficient block based feature level multiwavelet transform with neural network (BFMN) model for image fusion. The proposed (BFMN) model integrates MWT with neural network, which is one of the feature extraction or detection machine learning applications. In the proposed BFMN model, the two fusion techniques, multiwavelet transform (MWT) and neural network (NN) are discussed for fusing the IRS-1D images using LISS III scanner about the locations Hyderabad, Vishakhapatnam, Mahaboobnagar and Patancheru in Andhra Pradesh, India. Also QuickBird image data and Landsat 7 image data are used to perform experiments on the proposed BFMN model. 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 BFMN model is compared with other techniques to assess the quality of the fused image. Experimental results clearly prove that the proposed BFMN model is an efficient and feasible algorithm for image fusion.
Keywords: Image Fusion, Multiwavelet Transform, GHM multiwavelet, Mutual information, Performance Analysis.