Quantitative analysis of fused infrared and visible images using spatial and transform domain techniques

Image Fusion is a methodology of joining useful data of infrared and visible images into a single huge informative image. The infrared images are mostly dark and only more heat emitting objects can identify. The visible image provides more information about their surroundings. So to capture the both objects and surroundings we need to fuse both the images. In this paper we are analyzing and comparing different spatial and transform domain fusion methods like Average, Principal Component Analysis (PCA), Laplacian Pyramid Approach (LPA), Contrast Pyramid Approach (CPA), Discrete Wavelet Transform (DWT), Morphological Pyramid Approach (MPA) with Quantitative measures like Entropy (EN) and Peak Signal to Noise Ratio (PSNR). From the experimental results observed that Principal Component Analysis provided better information using EN and PSNR measures for all the image sets is same and satisfied with the visualization of fused image also.