Perceptual evaluation of different image fusion schemes

An Improved Infrared and Visible Image Fusion Algorithm Based On Curvelet Transform

“…Thus, the fusion of infrared images and visible images has been widely applied to obtain complementary image information, with which we can better describe a scene and complete some tasks, such as target detection and localization, environment identification [10][11].…”

Cognitive Image Fusion and Assessment

“…This may lead to a performance that is even worse compared to single band imagery alone (Sinai et al, 1999a). Experiments have indeed convincingly demonstrated that a false color rendering of fused night-time imagery which resembles natural color imagery significantly improves observer performance and reaction times in tasks that involve scene segmentation and classification (Essock et al, 1999;Sinai et al, 1999b;Toet et al, 1997a;Toet & IJspeert, 2001;Vargo, 1999;White, 1998), whereas color mappings that produce counterintuitive (unnaturally looking) results are detrimental to human performance (Krebs et al, 1998;Toet & IJspeert, 2001;Vargo, 1999). One of the reasons often cited for inconsistent color mapping is a lack of physical color constancy (Vargo, 1999).…”

“…However, until now the human end user has not been involved in the design process and the development of image fusion algorithms to any great extent. Mostly, image fusion algorithms are developed in isolation, and the human end-user is little more than an afterthought, so that separate follow-up evaluation studies are usually required to assess to what extent humans benefit from these methods (Aguilar et al, 1999;Dixon et al, 2005;Dixon et al, 2006a;Dixon et al, 2006b;Essock et al, 1999;Essock et al, 2005;Krebs & Sinai, 2002;Smith et al, 2002;Toet & Franken, 2003;Waxman et al, 2006). Recently has it been realized that the only way to guarantee the ultimate effectiveness of image fusion methods for human observers is to include human evaluation as an integral part of the design process (Muller & Narayanan, 2009).…”

Multispectral image fusion for improved RGB representation based on perceptual attributes

“…The method proposed in this work aims to derive a colour image of improved quality and fidelity that will be used mainly for visual interpretation. Therefore, the overall performance evaluation is based on perceptual evaluation as in Achalakul and Taylor (2001), Tyo et al (2003), Bogogni and Hansen (2001) and Toet and Franken (2003). In recent years, a few objective measures for the evaluation of fused methods have been proposed (Xydeas andPetrovic 2000, Qu et al 2002).…”