A human perception inspired quality metric for image fusion based on regional information

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.…”

Image Fusion for Remote Sensing Applications

“…Other methods for assessing fusion quality have been proposed (Liu et al, 2008;Chen and Varshney, 2007;Zheng & Chin;Zheng et al, 2008;Chen & Blum, 2009;Wang et al, 2008). Liu et al (2008) proposed two metrics based on a modified structural similarity measure (FSSIM) scheme and the local cross-correlation between the feature maps of the fused and input images.…”

“…These metrics provide an objective quality measure in the absence of a reference image. Chen & Varshney (2007) proposed a new quality metric for image fusion that does not require a reference image. It is based on local information given by a set of localized windows and by the difference in the frequency domain filtered by a contrast sensitivity function.…”

Cognitive Image Fusion and Assessment

“…In recent years, a number of computational image fusion quality assessment metrics have therefore been proposed (e.g. Angell, 2005;Blum, 2006;Chari et al, 2005;Chen & Varshney, 2005;Chen & Varshney, 2007;Corsini et al, 2006;Cvejic et al, 2005a;Cvejic et al, 2005b;Piella & Heijmans, 2003;Toet & Hogervorst, 2003;Tsagiris & Anastassopoulos, 2004;Ulug & Claire, 2000;Wang & Shen, 2006;Xydeas & Petrovic, 2000;Yang et al, 2007;Zheng et al, 2007;Zhu & Jia, 2005). 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.…”