基于显著性检测的声呐图像快速降噪研究
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摘要
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( BM3 D) 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 BM3 D 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 BM3 D,while the image visual effect is guaranteed,which is satisfied with the real-time application requirement of autonomous underwater vehicles.
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( BM3 D) 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 BM3 D 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 BM3 D,while the image visual effect is guaranteed,which is satisfied with the real-time application requirement of autonomous underwater vehicles.
引文
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[2] FEI T,KRAUS D,ZOUBIR A M. Contributions to Automatic Target Recognition Systems for Underwater Mine Classification[J]. IEEE Trans on Geoscience&Remote Sensing,2014,53(1):505-518
[3] HEL-OR Y,SHAKED D. A Discriminative Approach for Wavelet Denoising[J]. IEEE Trans on Image Processing,2008,17(4):443-457
[4] BUADES A,COLL B,MOREL J M. A Non-Local Algorithm for Image Denoising[C]∥IEEE International Conference on Computer Vision and Pattern Recognition,2005,2:60-65
[5] DABOV K,FOI A,KATKOVNIK V,et al. Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering[J].IEEE Trans on Image Processing,2007,16(8):2080-2095
[6] LEBRUN M. An Analysis and Implementation of the BM3D Image Denoising Method[J]. Image Processing on Line,2012,2(25):175-213
[7] DABOV K,FOI A,KATKOVNIK V,et al. Color Image Denoising via Sparse 3D Collaborative Filtering with Grouping Constraint in Luminance-Chrominance Space[C]∥IEEE International Conference on Image Processing,2007:313-316
[8] MAGGIONI M,FOI A. Joint Removal of Random and Fixed-Pattern Noise through Spatiotemporal Video Filtering[J]. IEEE Trans on Image Processing,2014,23(10):4282-4296
[9]张哲熙.基于BM3D的图像去噪算法研究[D].西安:西安电子科技大学,2017ZHANG Zhexi. Research on Image Denoising Algorithm Based on BM3D[D]. Xi'an,Xidian University,2017(in Chinese)
[10] ZHU W,LIANG S,WEI Y,et al. Saliency Optimization from Robust Background Detection[C]∥IEEE International Conference on Computer Vision and Pattern Recognition,2014:2814-2821
[11]郑雄波,张晓威.多小波变换在声纳图像降噪中的应用研究[J].数值计算与计算机应用,2011,32(2):89-96ZHENG Xiongbo,ZHANG Xiaowei. Study to the Application of Multiwavelets Transforms in Sonar Image Denoising[J]. Journal on Numerical Methods and Computer Applications,2011,32(2):89-96(in Chinese)
[12]DAVIS A,LUGSDIN A. High Speed Underwater Inspection for Port and Harbour Security Using Coda Echoscope 3D Sonar[C]∥IEEE International Conference on Oceans,2005:2006-2011
[13]ARRINGTON C M,CARR T H,MAYER A R,et al. Neural Mechanisms of Visual Attention:Object-Based Selection of a Region in Space[J]. Journal of Conference Neuroscience,2014,12(suppl 2):106-117
[14]ACHANTA R,SHAJI A,SMITH K,et al. SLIC Superpixels[R]. EPFL Technical Report 149300,2010
[15]YANG C,ZHANG L,LU H,et al. Saliency Detection via Graph-Based Manifold Ranking[C]∥IEEE International Conference on Computer Vision and Pattern Recognition,2013:3166-3173
[16]霍冠英,刘静,李庆武,等.空间约束FCM与MRF结合的侧扫声呐图像分割算法[J].仪器仪表学报,2017,38(1):226-235HUO Guanying,LIU Jing,LI Qingwu,et al. Side-Scan Sonar Image Segmentation Algorithm Based on Space-Constrained FCM and MRF[J]. Chinese Journal of Scientific Instrument,2017,38(1):226-235(in Chinese)