Uniform Based Approach for Image Fusion

Abstract

This paper presents uniform based image fusion algorithm. Image fusion is a process of combining the source images to acquire the relevant information which is nearer to the original image. Source images are divided into sub blocks. Smoothness of the each block is calculated using variance of the block. In general most of the images are affected by Gaussian noise [1]. Hence, in this work a new image is generated based on blocks which have more smoothness. By considering smoothed blocks alone in both the images, as a result, most of the Gaussian noise is eliminated. Further, different pixel based algorithms (average, max-abs, and min-abs) are tested with the uniform based algorithm. Performance of different fused algorithms is assessed by using Peak Signal to Noise Ratio (PSNR), Mutual Information (MI), Edge Strength and Orientation Preservation (ESOP), Normalized Cross Correlation (NCC), and Feature Similarity (FSIM).