Sira Ferradans

An Analysis of Visual Adaptation and Contrast Perception for Tone Mapping

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.

Geometry-Based Demosaicking

Demosaicking is a particular case of interpolation problems where, from a scalar image in which each pixel has either the red, the green or the blue component, we want to interpolate the full-color image. State-of-the-art demosaicking algorithms perform interpolation along edges, but these edges are estimated locally. We propose a level-set-based geometric method to estimate image edges, inspired by the image in-painting literature. This method has a time complexity of O(S) , where S is the number of pixels in the image, and compares favorably with the state-of-the-art algorithms both visually and in most relevant image quality measures.

An Algorithmic Analysis of Variational Models for Perceptual Local Contrast Enhancement

Color cast cancellation and local contrast enhancement are very important problems in computer vision. In this paper we review the algorithm proposed by Palma-Amestoy et al. [A perceptually inspired variational framework for color enhancement, IEEE Transactions on Pattern Analysis and Machine Intelligence, 21 (2009), pp. 458–474], present results and evaluate the impact of a change in the parameters.

A Multi-Modal Approach to Perceptual Tone Mapping

We present an improvement of TSTM, a recently proposed tone mapping operator for High Dynamic Range (HDR) images, based on a multi-modal analysis. One of the key features of TSTM is a suitable implementation of the Naka-Rushton equation that mimics the visual adaptation performed by the human visual system coherently with Weber-Fechner’s law of contrast perception. In the present paper we use the Gaussian Mixture Model (GMM) in order to detect the modes of the log-scale luminance histogram of a given HDR image and then we use the information provided by GMM to properly devise a Naka-Rushton equation for each mode. Finally, we properly select the parameters in order to merge those equations into a continuous function. Tests and comparisons to show how this new method is capable of improving the performances of TSTM are provided and commented, as well as comparisons with state of the art methods.