<title>Performance comparison of different gray-level image fusion schemes through a universal image quality index</title>

Abstract

We applied a recently introduced universal image quality index Q that quantifies the distortion of a processed image relative to its original version, to assess the performance of different graylevel image fusion schemes. The method is as follows. First, we adopt an original test image as the reference image. Second, we produce several distorted versions of this reference image. The distortions in the individual images are complementary, meaning that the same distortion should not occur at the same location in all images (it should be absent in at least one image). Thus, the information content of the overall set of distorted images should equal the information content of the original test image. Third, we apply the image fusion process to the set of distorted images. Fourth, we quantify the similarity of the fused image to the reference image by computing the universal image quality index Q. The method can also be used to optimize image fusion schemes for different types of distortions, by maximizing Q through repeated application of steps two and three for different parameter settings of the fusion scheme.

Towards cognitive image fusion

“…However, this usually means time consuming and often expensive experiments involving a large number of human subjects. In recent years, a number of computational image fusion quality assessment metrics have therefore been proposed [2,3,[5][6][7][12][13][14]36,42,44,46,49,[52][53][54][55]. Although some of these metrics agree with human visual perception to some extent, most of them cannot predict observer performance for different input imagery and scenarios.…”

Theoretical analysis of correlation-based quality measures for weighted averaging image fusion

“…(12)) except that r s ; r 1 and r 2 in Eq. (12) are replaced by r 0 s ; r 0 1 and r 0 2 . We can see that although the images are filtered by an edge detection operator, the noise still occurs in the edge information of the input and fused images inevitably.…”

“…Measuring the performance of image fusion algorithms is an extremely important task which has received past study [3,4,[7][8][9][10][11][12]. The vast majority of investigations have been focused on developing approaches suitable for experimental evaluation of fused images.…”

On multisensor image fusion performance limits from an estimation theory perspective

“…Measuring the performance of image fusion algorithms is an extremely important task which has received past study [46][47][48][49][50][51][52][53]. The vast majority of investigations have been focused on developing approaches suitable for experimental evaluation of fused images.…”