A Probabilistic Method for Image Enhancement With Simultaneous Illumination and Reflectance Estimation

In this paper, a new probabilistic method for image enhancement is presented based on a simultaneous estimation of illumination and reflectance in the linear domain. We show that the linear domain model can better represent prior information for better estimation of reflectance and illumination than the logarithmic domain. A maximum a posteriori (MAP) formulation is employed with priors of both illumination and reflectance. To estimate illumination and reflectance effectively, an alternating direction method of multipliers is adopted to solve the MAP problem. The experimental results show the satisfactory performance of the proposed method to obtain reflectance and illumination with visually pleasing enhanced results and a promising convergence rate. Compared with other testing methods, the proposed method yields comparable or better results on both subjective and objective assessments.

I. Introduction

Based on the simplification of light reflection, an observed image can be modeled as the product of the illumination and the reflectance [1]. Many methods that decompose an image into the illumination and the reflectance have been used in a series of applications, such as contrast enhancement [2]–[4], non-uniform illumination images enhancement [5], tone mapping [6]–[10], remote sensing image correction [11], image segmentation [12] and target selection and tracking [13], [14], etc.