QBRIX : a quantile-based approach to retinex

In this paper, we introduce a novel probabilistic version of retinex. It is based on a probabilistic formalization of the random spray retinex sampling and contributes to the investigation of the spatial properties of the model. Various versions available of the retinex algorithm are characterized by different procedures for exploring the image content (so as to obtain, for each pixel, a reference white value), then used to rescale the pixel lightness. Here we propose an alternative procedure, which computes the reference white value from the percentile values of the pixel population. We formalize two versions of the algorithm: one with global and one with local behavior, characterized by different computational costs

Bio-inspired image enhancement for natural color images

Capturing and rendering an image that fulfills the observer's expectations is a difficult task. This is due to the fact that the signal reaching the eye is processed by a complex mechanism before forming a percept, whereas a capturing device only retains the physical value of light intensities. It is especially difficult to render complex scenes with highly varying luminances. For example, a picture taken inside a room where objects are visible through the windows will not be rendered correctly by a global technique. Either details in the dim room will be hidden in shadow or the objects viewed through the window will be too bright. The image has to be treated locally to resemble more closely to what the observer remembers. The purpose of this work is to develop a technique for rendering images based on human local adaptation. We take inspiration from a model of color vision called Retinex. This model determines the perceived color given spatial relationships of the captured signals. Retinex has been used as a computational model for image rendering. In this article, we propose a new solution inspired by Retinex that is based on a single filter applied to the luminance channel. All parameters are image-dependent so that the process requires no parameter tuning. That makes the method more exible than other existing ones. The presented results show that our method suitably enhances high dynamic range images

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Unsupervised corrections of unknown chromatic dominants using a Brownian-path-based retinex algorithm

An experimental analysis of chromatic equalization based on a new implementation of the Retinex algorithm is presented. The experiments are carried out on a colored Mondrian patchwork illuminated with different commercial light sources and on synthetic images generated with a photometric ray tracer using different illuminants. Regarding the Mondrian patchwork, the spectral characteristics of the bulbs and the reflected light from each patch are measured using a commercial spectrometer From the measured data, synthetic images of the patchwork with different illuminants are created and processed by the Retinex algorithm. The chromatic correction capabilities of the Retinex implementation have been measured and compared with unfiltered values and with the results of another Retinex implementation and classic color equalization algorithms. Results show that Retinex performs an unsupervised color correction without requiring any information about the spectral composition of the illuminant