Joint desmoking, specularity removal, and denoising of laparoscopy images via graphical models and Bayesian inference

Laparoscopic images exhibit artifacts resulting from surgical smoke, specular highlights, and noise. These artifacts degrade the results of subsequent processing (e.g., tracking, segmentation, and depth analysis) and compromise surgical quality. We formulate a unified Bayesian inference problem for desmoking, specularity removal, and denoising in laparoscopic images. We propose novel probabilistic graphical models and sparse dictionary models as image priors. For inference, we rely on variational Bayesian expectation maximization. Results on simulated and real-world laparoscopic images, including clinical expert evaluation, show that our joint optimization method outperforms the state of the art.