Image fusion can be used to improve the image utilization, spatial resolution and spectral resolution, which has been widely applied on medicine, remote sensing, computer vision, weather forecast and military target recognition. The goal of image fusion is to reduce the uncertainty and redundancy of the output and increase the reliability of the image on the basis of the maximum combination of relevant information. In this paper, a multi-focus image fusion algorithm based on WNMF and Focal point position analysis is proposed to improve the image fusion method based on nonnegative matrix factorization. In the imaging process, the Gaussian function is used to approximate the point spread function in the optical system. Then calculate the difference between the original image and the approximate point spread function and get the weighted matrix [Formula: see text]. Finally, we apply the weighted nonnegative matrix algorithm to image fusion, and the new fusion image with clear parts is obtained. Experimental results show that the multi-focus image fusion algorithm based on WNMF and Focal point position analysis (MFWF) is better.
Face Recognition Using a Gabor Filter Bank Approach
