An Analysis of Visual Adaptation and Contrast Perception for Tone Mapping

Abstract

Tone Mapping is the problem of compressing the range of a High-Dynamic Range image so that it can be displayed in a Low-Dynamic Range screen, without losing or introducing novel details: The final image should produce in the observer a sensation as close as possible to the perception produced by the real-world scene. We propose a tone mapping operator with two stages. The first stage is a global method that implements visual adaptation, based on experiments on human perception, in particular we point out the importance of cone saturation. The second stage performs local contrast enhancement, based on a variational model inspired by color vision phenomenology. We evaluate this method with a metric validated by psychophysical experiments and, in terms of this metric, our method compares very well with the state of the art.

Duality Principle for Image Regularization and Perceptual Color Correction Models

“…1(a)) the proposed vectorial extensions of the regularization model of Gilboa and Osher [15], whose unique solution is given in (8), and the perceptual color correction model of Bertalmío et al [4], whose solutions are given in (14). On this latter model, we have set the constant β to 0, i.e.…”

“…Regarding contrast modification, the pioneer variational approach is due to Sapiro and Caselles [23], who performed contrast enhancement for histogram equalization purpose. Since then, this variational formulation has been generalized and applied in different contexts: perceptual color correction [4], [20], tone mapping [14], and gamut mapping [25] to name a few. Connections have also been made between the variational formulation [4] and Retinex theory [5], [6].…”

A Variational Method for the Optimization of Tone Mapping Operators

“…Considering the metric as a non local operator, we are led to a non local variational problem where the quantity to be minimized is a distance associated to the metric. We test our method on the TMOs of Drago et al [5], Ferradans et al [9], Mantiuk et al [15], Reinhard et al [20]. Experiments show that our method appreciably reduces contrast distortion in the results of the aforementioned TMOs.…”

“…As mentioned in Ferradans et al [9], TMOs might be classified into two categories. There is a category of TMOs inspired by vision models, like Retinex theory [10], anchoring theory [11], Naka-Rushton formula ( [18], [20], [12]), WeberFechner's law ( [2], [25]).…”

Enhanced Variational Image Dehazing

“…(3.1) is not adequate, since we want to respect the colors of the haze-free image, not to correct the illuminant of the scene. Different modifications of this hypothesis have already been proposed for several problems [10,50]. Here, to approximately predict which should be the mean value of a dehazed scene, we rely on the model of Eq.…”