Assessment method to fusion effect based on structural similarity comparison in fusion images

Abstract

Image fusion can integrate several images of the same scene captured by several different sensors with different features and resolutions at different time into one image. Research on quality assessment of fusion images is meaningful for image processing course in order to improve the registration technology and fusion algorithm. Structural similarity metric describes differences between two images by means of three variables, luminance, contrast, and spatial similarity, which show the better evaluating capability than others objective metrics. A new assessment method to fusion effect based on structural similarity comparison among fusion images is provided in paper. Fusion algorithms including weighing method, principal component analysis, different pyramid methods and multi-resolution wavelet filtering is used to create fusion images. Then the mutual structural similarity metric among fusion images obtained by different fusion algorithms is used to evaluate the fusion effect. In some extent, the low structural similarity comparison denotes the low quality fusion effect. Meanwhile, the experiment show also the fusion effect determined by structural similarity comparison is accordant with the subjective evaluation. Besides, the experiment explain the method based on different pyramid methods and multi-resolution wavelet filtering have the better fusion effect than weighing method and principal component analysis method. Furthermore, the experiment also prove the whole image fusion system should choose the different fusion algorithm to adjust to the different task requirement and applied circumstance in order to acquire the optimum scene interpreting effect.