Using a MRF-BP model with color adaptive training for underwater color restoration

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For underwater robotics applications involving monitoring and inspection tasks, it is important to capture quality color images in real time. In this paper, we propose a statistically learning method with an automatic selection of the training set for restoring the color of underwater images. Our statistical model is a Markov Random Field with Belief Propagation (MRF-BP). The quality of the results depends strongly on the trained correlations between the degraded image and its corresponding color image. However, it is not possible to have color ground truth data given the inherent conditions of underwater environments. Thus, we build a color adaptive training set by applying a multiple color space analysis to those frames that present a high change in its distribution from the previous frame and use only those frames for training. Experimental results in real underwater video sequences demonstrate that our approach is feasible, even when visibility conditions are poor, as our method can recover and discriminate between different colors in objects that may seem similar to the human eye.