Learning-based underwater image enhancement with adaptive color mapping

Blurring and color cast are two of the most challenging problems for underwater imaging. The poor quality hinders the automatic segmentation or analysis of images. In this paper, we describe an image enhancement method to reduce the blurring and color cast of the underwater medium. It is a two-folded approach; First, a color correction algorithm is applied to correct the color cast and produce a natural appearance of the sub-sea images. Second, a pair of learned dictionaries based on sparse representation are applied to sharpen the image and enhance the details. Our strategy is a single image approach that does not require additional knowledge of environment such as depth, distance object/camera or water quality. The experimental results show that the proposed method can efficiently enhance almost every underwater image; And offers a quality that is typically sufficient for the high level computer vision algorithms.