Abstract. 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.
In this paper, we propose and discuss some approaches for measuring perceptual contrast in digital images. We start from previous algorithms by implementing different local measures of contrast and a parameterized way to recombine local contrast maps and color channels. We propose the idea of recombining the local contrast maps and the channels using particular measures taken from the image itself as weighting parameters. Exhaustive tests and results are presented and discussed, in particular we compare the performance of each algorithm in relation to perceived contrast by observers. Current results show an improvement in correlation between contrast measures and observers perceived contrast when the variance of the three color channels separately is used as weighting parameter for local contrast maps.
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