Turbid Underwater Image Enhancement via Attenuation Prior Formation Model

Abstract

To deal with the issue of poor visibility caused by water turbidity during the operation of underwater robotics, we propose an attenuation prior formation model-guided enhancement algorithm for turbid underwater images. Specifically, we establish an imaging model suitable for turbid water by studying the influence of water turbidity on light attenuation and transmission. For this model, we first propose a scoring formula that takes into account multiple prior knowledge to estimate the global background light with the help of a hierarchical searching technique. Then, we make full use of the advantages of different scale neighborhoods in image restoration and propose an adaptive multiscale weighted fusion transmission estimation method to balance brightness and contrast. In addition, to correct the color of the images with a natural appearance, a variation of white balance is introduced as postprocessing. Extensive experiments on two image data sets show that our algorithm achieves better results than state-of-the-art methods.