The Review of Feature Level Fusion of Multi-Focused Images Using Wavelets

Abstract

Abstract: The fast development of digital image processing leads to the growth of feature extraction of images which leads to the development of Image fusion. 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. The most common widely used transform for image fusion at multi scale is Discrete Wavelet Transform since it minimizes structural distortions. But, wavelet transform suffers from lack of shift invariance and poor directional selectivity. These two disadvantages are overcome by Stationary and Complex Wavelet Transform. But they are more expansive and this can be compromised by Double Density Wavelet Transform. Image fusion can be performed using three levels namely Pixel, feature and decision level. This paper evaluates the performance of feature level fusion of multi focused images using Discrete, Stationary and Dual Tree Complex wavelet transform in terms of various performance measures.