Area level fusion of Multi-focused Images using Multi-Stationary Wavelet Packet Transform

Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely Spatial Fusion and Transform fusion. In Spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the composite image at that location. Transform fusion uses transform for representing the source images at multi scale. The most common widely used transform for image fusion at multi scale is Wavelet Transform since it minimizes structural distortions. But, wavelet transform suffers from lack of shift invariance & poor directionality and Stationary Wavelet Transform and Wavelet Packet Transform overcome these disadvantages. The Multi-Wavelet Transform of image signals produces a non-redundant image representation, which provides better spatial and spectral localization of image formation than discrete wavelet transform. In this paper, Multi-Wavelet Transform, Stationary Wavelet Transform and Wavelet Packet Transform were combined to form Multi-Stationary Wavelet Packet Transform and its performance in fusion of multi-focused images in terms of Peak Signal to Noise Ratio, Root Mean Square Error, Quality Index and Normalized Weighted Performance