基于显著性检测的声呐图像快速降噪研究

Fast Denoising of Sonar Image Based on Saliency Detection

声呐图像在获取过程中易受噪声污染,而降噪性能好的算法通常时间复杂度较高。鉴于人类视觉注意机制,将基于流形排序(MR)的显著性检测方法引入声呐图像处理,将图像自动分割为显著区域和非显著区域两部分。对于占比小的显著区域采用三维块匹配(BM3D)算法降噪以保护图像主要信息,对非显著背景区域采用执行效率较高的均值滤波(MF)算法。将所提算法同经典MF,BM3D算法进行主观和客观评价指标对比,结果表明,所提算法在提高图像视觉效果的同时,执行时间较BM3D算法大为减少,可以满足水下航行器实时作业的应用需求。

Sonar image is inevitably contaminated by noise during the acquisition process, while the noise reduction algorithms with good performance are usually of high time complexity. The human visual attention mechanism usually guides the eyes to salient region and gives priority to those visual information. In view of this, saliency detection based on manifold ranking was introduced into sonar image processing, to divide the image into two parts: salient region and non-significant region. For the salient region with small proportion, block-matching and 3-D filtering (BM3D) algorithm was adopted to reduce noise and protect the main information of the image;for the non-significant background which was not very concerned, high efficiency mean filtering was used. On the collected sonar image data set, the present algorithm was compared with the classic MF and BM3D algorithms through the subjective and 2 objective evaluation indexes. The experimental results show that the efficiency of the present algorithm is much higher than that of BM3D, while the image visual effect is guaranteed, which is satisfied with the real-time application requirement of autonomous underwater vehicles.