Novel Retinex-type Images Enhancement Method based on Sampling Representations

The work is devoted to the issues of statistical processing of images based on the modeling of some mechanisms of perception on the periphery of the human visual system (the retina). First of all, we mean the processes of coding (compression) of information, registered by receptors for its compact transmission through the visual channel, i.e. the optic nerve. So the synthesis of the proposed coding is based on the known mechanisms of neural layered processing of registered radiation. One of the features of such a coding is the enhancement of the illumination/reflectivity in the field of view. The main idea of such enhancement is formalized in the frames of the so-called Retinex model. Second, the classical approach to the coding synthesis is the Rate–Distortion theory. However, it has been established over the past two decades, that an optimal ratio of rate and distortion does not guarantee high perceptual image quality. It is because the coding also needs to have some predictive aspects. In this regard, the information bottleneck principle, proposed around 2000, proved to be very promising for solving the Rate-Distortion-Perception tradeoff. In this work we offer some insights for implementing this principal into Retinex-type images enhancement. Namely, we propose a generative model of an image encoder that provides an optimal compromise between the degree of data compression and the predictive (perceptual) quality of the code. The generative model of such an encoder opens up the way to the synthesis of image encoding methods based on the known principles of statistical (machine) learning, such as: multiresolution analysis, nonlinear (adaptive) filtering, neural networks, etc. For an adequate construction of a generative model of image / neural coding, a number of formalized descriptions are used in the paper. They are, firstly, the description of the recorded data through a special representation of images by a controlled size samples of counts (sampling representations) and, secondly, a description of the neural coding of the recorded data, carried out by bipolar / ganglion neurons through a system of receptive fields. The main characteristics of the synthesized coding methods are illustrated by the results of computer simulation.