An Efficient Block based Feature Level Image Fusion Technique using Wavelet Transform and Neural Network

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. General TermsDiscrete wavelet transform, neural network

Depth Analysis of Single View Image Objects based on Object Detection and Focus Measure

“…When depth of the wavelet decomposition increases, the robustness increases. In the experiments, level 2 produces sufficient robustness [5]. Mostly the images with same objects of roughly same size is considered so that the computed focus measures are justified e.g.…”

“…When an image is considered, then its Wavelet Transform has explained as a ratio of high pass and low pass norms. Discrete Wavelet Transform and Discrete Cosine Transform are same when there comes a conversion of spatial to frequency domain [5]. In Discrete Wavelet Transform, the image splits in 4 sub images.…”

“…In it, a 1 is original image's approximation. b 1 , b 2 and b 3 are the sub images that consist of diagonal, horizontal and vertical components of the original input image [5]. Further, DWT can be applied on a 1 and a 2 level DWT breakdown is obtained that result in 7 sub images which is shown in Figure.…”

“…1 (b). The procedure may continue to produce decomposition up to n th level [5]. The coefficient w is not varying as per the measure of the image.…”

“…In paper [5], the authors deliberated about the combination of Neural Network and Wavelet Transform. In this paper, mixture of two techniques, i.e.…”