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

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<p>An important step in 3D data generation is the generation of depth map. Depth map is a black and white image which has exactly the same size of the original captured 2D image that indicates the relative distance of each pixel from the observer to the objects in the real world. This paper presents a survey of Depth Perception from Defocused or blurs images as well as image from motion. The change of distance of the object from the camera has direct relation with the amount of blurring of object in the image. The amount of blurring will be calculated with a comparison in front of the camera directly and can be seen with the changes at gray level around the edges of objects.</p>