Dynamic weighted non-negative matrix factorization and its using research in image fusion

The image fusion algorithm based on standard non-negative matrix factorization has limited global feature extraction ability, resulting in the low contrast and poor visual effect of the fusion result. In order to improve the fusion effect of non-negative matrix factorization, a novel image fusion algorithm for infrared and visible images based on dynamic weighted non-negative matrix factorization is proposed. The weighted coefficients are designed for emphasizing the important characteristics of the source images, and they are modified after every iterative according to the relative importance variation of different areas, so the feature extraction ability of weighted non-negative matrix factorization is enhanced obviously. Compared with standard non-negative matrix factorization based fusion algorithm, proposed algorithm improves the visual effect, and the average gradient of the fusion result improves more than 36% and the standard deviation improves more than 17%.