An Underwater Laser Image Segmentation Algorithm Based on Pulse Coupled Neural Network and Morphology

Range-gated underwater laser imaging technology, which is very promising in oceanic research, deep sea exploration, and robotic works, is one of the most effective methods to suppress the effect of backward scattering of water medium. However, the special features of underwater laser images, such as speckle noise and nonuniform illumination, bring great difficulty for image segmentation. In this paper, an image segmentation algorithm which combines improved pulse coupled neural network with morphology is proposed. The morphology is applied to eliminate the speckle noise, while the cross-entropy is calculated as an optimization criterion for determination of the optimal segmentation. The experimental results of the proposed algorithm are compared with those of NCut, Mean-shift, Fuzzy C-means, and Watershed methods, and the quantitative evaluation confirms that the proposed algorithm is significantly superior to the other four algorithms in segmentation accuracy and robustness against speckle noise and nonuniform illumination.