Underwater image segmentation based on particle swarm optimization and fuzzy partition entropy
Qin Zai-bai · Optical Technique · 2007
Due to the assimilation of the water and uneven lightness,the underwater images would have low S/N and the detail is fuzzy.If traditional methods are used to dispose underwater images directly,it is unlikely to obtain satisfactory results.Though traditional threshold methods based on maximum entropy principle could sometimes divide the image into object and background,its time-consuming computation is often an obstacle.Particle swarm optimization(PSO) is a stochastic global optimization technique,which has become the hotspot of evolutionary computation because of its excellent performance and simple for implement.The particle swarm optimization algorithm is applied into the image segmentation.In the method,fuzzy entropy is redefined on given images,and the particle swarm optimization algorithm is used to search the optimal threshold based on maximum entropy principle.The experiments prove that this novel approach is effective for simple back-grounded underwater images.Comparing with the traditional methods,the new method shows better adaptability and noise restraining performance.