The authors present a new framework for algorithms for a wide range of image enhancement and reproduction applications, named STRESS Spatio-Temporal Retinex-inspired Envelope with Stochastic Sampling. The algorithms work by recalculating each pixel using envelopes for local upper and lower bounds in the image. The envelopes are obtained sampling neighbor pixels and can be interpreted as local reference maximum and minimum. This approach derives from a computational simplification of previous spatial color algorithms like Retinex or ACE. With the proposed method, various tasks such as local contrast stretching, automatic color correction, high dynamic range image rendering, spatial color gamut mapping, and color to grayscale conversion can be performed with good results. The algorithm exhibits behaviors in line with some aspects of the human visual system, e.g., simultaneous contrast

The original presentation of Retinex, a spatial color correction and image enhancement algorithm modeling the human vision system, as proposed by Land and McCann in 1964, uses paths to explore the image in search of a local reference white point. The interesting results of this algorithm have led to the development of many versions of Retinex. They follow the same principle but differ in the way they explore the image, with, for example, random paths, random samples, convolution masks, and variational formulations. We propose an alternative way to explore local properties of Retinex, replacing random paths by traces of a specialized swarm of termites. In presenting the spatial characteristics of the proposed method, we discuss differences in path exploration with other Retinex implementations. Experiments, results, and comparisons are presented to test the efficacy of the proposed Retinex implementation.

It is well established from both colour difference and colour order perpectives that the colour space cannot be Euclidean. In spite of this, most colour spaces still in use today are Euclidean, and the best Euclidean colour metrics are performing comparably to state-of-the-art non-Euclidean metrics. In this paper, it is shown that a transformation from Euclidean to hyperbolic geometry (i.e., constant negative curvature) for the chromatic plane can significantly improve the performance of Euclidean colour metrics to the point where they are statistically significantly better than state-of-the-art non-Euclidean metrics on standard data sets. The resulting hyperbolic geometry nicely models both qualitatively and quantitatively the hue super-importance phenomenon observed in colour order systems.