Restoration of Unevenly Illuminated Images

In this paper, we tackle the problem of restoring unevenly illuminated images. Generally, there exist three kinds of exposure conditions in these images: under-, normal-, and over-exposures. Thus, a three-component generalized Gaussian mixture model (3GGMM) is used to fit the histogram of the illuminance image, and probabilistically characterize the three exposure states. Based on the 3GGMM, separate optimal tone mapping functions are designed to enhance under- and overexposed regions by maximizing expected contrast of these regions. The output illumination can be obtained by fusing the restoration results in different exposure states. Experimental results validate the effectiveness of the proposed image restoration approach.