FPGA-Based Multi-Focus Image Fusion Techniques

Image fusion is a process which combines the data from two or more source images from the same scene to generate one single image containing more precise details of the scene than any of the source images. Among many image fusion methods like averaging, principle component analysis and various types of Pyramid Transforms, Discrete cosine transform, Discrete Wavelet Transform special frequency and ANN and they are the most common approaches. In this paper multi-focus image is used as a case study. This paper addresses these issues in image fusion: Fused two images by different techniques which present in this research, Quality assessment of fused images with above methods, Comparison of different techniques to determine the best approach and Implement the best technique by using Field Programmable Gate Arrays (FPGA). First a brief review of these techniques is presented and then each fusion method is performed on various images. In addition experimental results are quantitatively evaluated by calculation of ischolar_main mean square error, entropy; mutual information, standard deviation and peak signal to noise ratio measures for fused images and a comparison is accomplished between these methods. Then we chose the best techniques to implement them by FPGA.