An improved Bayesian algorithm for color image desaturation

Current digital imaging systems are unable to capture the entire dynamic range of the visible luminance, causing saturation in the very bright parts of a scene. Color distortion occurs when the amounts of saturation are different in the red (R), green (G), and blue (B) color channels. A Bayesian algorithm was developed in the past to correct the saturated pixels in raw images. For each image, it estimates the distributions of the R, G, and B color channels based on the unsaturated pixels, and then corrects the saturated pixels based on this prior distribution. In this paper, we improve this Bayesian algorithm by incorporating spatial information in the correction process. We utilize the strong spatial correlation of images as well as the correlation between the R, G, and B channels of each individual pixel to estimate the prior distributions of the R, G, and B color channels. The prior distribution of each saturated region is modeled individually based on its surrounding region, which is determined by morphological dilation. Experimental results show that our modified algorithm greatly outperforms the original Bayesian algorithm for fixing saturated pixels in color images.