Retinex theory estimates the human color sensation at any observed point by correcting its color based on the spatial arrangement of the colors in proximate regions. We revise two recent path-based, edge-aware Retinex implementations: Termite Retinex (TR) and Energy-driven Termite Retinex (ETR). As the original Retinex implementation, TR and ETR scan the neighborhood of any image pixel by paths and rescale their chromatic intensities by intensity levels computed by reworking the colors of the pixels on the paths. Our interest in TR and ETR is due to their unique, content-based scanning scheme, which uses the image edges to define the paths and exploits a swarm intelligence model for guiding the spatial exploration of the image. The exploration scheme of ETR has been showed to be particularly effective: its paths are local minima of an energy functional, designed to favor the sampling of image pixels highly relevant to color sensation. Nevertheless, since its computational complexity makes ETR poorly practicable, here we present a light version of it, named Light Energy-driven TR, and obtained from ETR by implementing a modified, optimized minimization procedure and by exploiting parallel computing.
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.
This paper describes a novel implementation of the Retinex algorithm with the exploration of the image done
by an ant swarm. In this case the purpose of the ant colony is not the optimization of some constraints but
is an alternative way to explore the image content as diffused as possible, with the possibility of tuning the
exploration parameters to the image content trying to better approach the Human Visual System behavior. For
this reason, we used "termites", instead of ants, to underline the idea of the eager exploration of the image.
The paper presents the spatial characteristics of locality and discusses differences in path exploration with other
Retinex implementations. Furthermore a psychophysical experiment has been carried out on eight images with
20 observers and results indicate that a termite swarm should investigate a particular region of an image to find
the local reference white.
Proc. SPIE. 7240, Human Vision and Electronic Imaging XIV
KEYWORDS: RGB color model, Colorimetry, Visualization, Electronic imaging, Visual system, Curium, Human vision and color perception, Current controlled current source, Electroluminescence, Image compression
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.