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

• Published in: IEEE transactions on image processing Vol. 24; no. 12; pp. 4965 - 4977
• Main Authors: Fu, Xueyang, Liao, Yinghao, Zeng, Delu, Huang, Yue, Zhang, Xiao-Ping, Ding, Xinghao
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational modeling; Estimation; illumination; Image color analysis; Image enhancement; Lighting; Mathematical model; maximum posterior probability (MAP); optimization methods; Probabilistic logic; reflectance; optimization methods; illumination; maximum posterior probability (MAP); reflectance; Image enhancement
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2015.2474701

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.