Multi-Modal Ship Target Image Smoothing Based on Adaptive Mean Shift

In this paper, we propose an adaptive image smoothing method for infrared (IR) and visual ship target images, aiming to effectively suppress noise as well as preserve important target structures, thus benefiting image segmentation. First, by analyzing the specific features of ship target images, a block based method combining local region mean and standard deviation is developed to highlight ship target regions. It is helpful to distinguish the ship target region from the background. Then, by associating the range bandwidth with local image properties of the ship target region and the background region, we develop an adaptive range bandwidth mean shift filtering method for IR and visual ship target image smoothing. With this proposed method, we can obtain a small bandwidth for ship target region and a large one for the background region. Therefore, we can effectively smooth the background while preserving the details of the targets. Experimental results show that this method works well for IR and visual ship target images with different backgrounds. The method demonstrates superior performance for image smoothing and target preservation compared with the four well-known edge-preserving denoising methods, including the anisotropic diffusion filtering, the bilateral filtering, the propagation filtering, and the mean shift filtering with fixed range bandwidth.

[1]  Thomas S. Huang,et al.  The Effect of Median Filtering on Edge Estimation and Detection , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  James Stephen Marron,et al.  Comparison of data-driven bandwith selectors , 1988 .

[3]  Azeddine Beghdadi,et al.  Contrast enhancement technique based on local detection of edges , 1989, Comput. Vis. Graph. Image Process..

[4]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Sung-Jea Ko,et al.  Center weighted median filters and their applications to image enhancement , 1991 .

[6]  M. C. Jones,et al.  A reliable data-based bandwidth selection method for kernel density estimation , 1991 .

[7]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[8]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[9]  Zhou Wang,et al.  Progressive switching median filter for the removal of impulse noise from highly corrupted images , 1999 .

[10]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Dorin Comaniciu,et al.  An Algorithm for Data-Driven Bandwidth Selection , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Taghi M. Khoshgoftaar,et al.  Efficient image segmentation by mean shift clustering and MDL-guided region merging , 2004, 16th IEEE International Conference on Tools with Artificial Intelligence.

[13]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[14]  Vladimir V. Khryashchev,et al.  Image denoising using adaptive switching median filter , 2005, IEEE International Conference on Image Processing 2005.

[15]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  Jean-Michel Morel,et al.  A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..

[17]  Leo Grady,et al.  Random Walks for Image Segmentation , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Pietro Perona,et al.  Graph-Based Visual Saliency , 2006, NIPS.

[19]  Jin Liu,et al.  Unified mean shift segmentation and graph region merging algorithm for infrared ship target segmentation , 2007 .

[20]  David Ebenezer,et al.  A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises , 2007, IEEE Signal Processing Letters.

[21]  Ronald G Driggers,et al.  Performance of infrared systems in swimmer detection for maritime security. , 2007, Optics express.

[22]  Guo Qingchang.  Image smoothing based on the mean-shift algorithm , 2007 .

[23]  Partha Sarathi Mukherjee,et al.  Edge structure preserving image denoising , 2010, Signal Process..

[24]  Jian Sun,et al.  Guided Image Filtering , 2010, ECCV.

[25]  Michael Unser,et al.  Fast $O(1)$ Bilateral Filtering Using Trigonometric Range Kernels , 2011, IEEE Transactions on Image Processing.

[26]  M. Wand,et al.  ASYMPTOTICS FOR GENERAL MULTIVARIATE KERNEL DENSITY DERIVATIVE ESTIMATORS , 2011 .

[27]  Alireza Behrad,et al.  Automatic marine targets detection using features based on Local Gabor Binary Pattern Histogram Sequence , 2011 .

[28]  Chu-Song Chen,et al.  From rareness to compactness: Contrast-aware image saliency detection , 2012, 2012 19th IEEE International Conference on Image Processing.

[29]  Jean-Michel Morel,et al.  A Nonlocal Bayesian Image Denoising Algorithm , 2013, SIAM J. Imaging Sci..

[30]  Zhou Wang,et al.  Image Sharpness Assessment Based on Local Phase Coherence , 2013, IEEE Transactions on Image Processing.

[31]  R. Jayashree.  RGB to HSI color space conversion via MACT algorithm , 2013, 2013 International Conference on Communication and Signal Processing.

[32]  Xiangzhi Bai,et al.  Automatic detection of ship target and motion direction in visual images , 2013 .

[33]  Nafis Uddin Khan,et al.  Edge preservation of impulse noise filtered images by improved anisotropic diffusion , 2013, Multimedia Tools and Applications.

[34]  J. E. Chac'on,et al.  A comparison of bandwidth selectors for mean shift clustering , 2013, 1310.7855.

[35]  Suman Shrestha.  Image Denoising using New Adaptive Based Median Filters , 2014, ArXiv.

[36]  Changming Sun,et al.  Infrared Ship Target Image Smoothing Based on Adaptive Mean Shift , 2014, 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA).

[37]  M. M. Babu,et al.  A Survey on De-Speckling of SAR Images , 2014 .

[38]  Wang Zhong-ce.  A Bilateral Filtering Based Image De-noising Algorithm for Nighttime Infrared Monitoring Images , 2014, 2014 International Conference on Computational Science and Computational Intelligence.

[39]  Wen Gao,et al.  Non-local extension of total variation regularization for image restoration , 2014, 2014 IEEE International Symposium on Circuits and Systems (ISCAS).

[40]  Yu-Chiang Frank Wang,et al.  Propagated image filtering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[41]  Vipin Tyagi,et al.  LAPB: Locally adaptive patch-based wavelet domain edge-preserving image denoising , 2015, Inf. Sci..

[42]  Ali Borji,et al.  Salient Object Detection: A Benchmark , 2015, IEEE Transactions on Image Processing.

[43]  Yu Zhang,et al.  Infrared Ship Target Segmentation Based on Spatial Information Improved FCM , 2016, IEEE Transactions on Cybernetics.

[44]  Jiangtao Xu,et al.  An improved anisotropic diffusion filter with semi-adaptive threshold for edge preservation , 2016, Signal Process..

[45]  Jason Jianjun Gu,et al.  Fast Weighted Total Variation Regularization Algorithm for Blur Identification and Image Restoration , 2016, IEEE Access.

[46]  Quan Zhang,et al.  A Novel Fractional-Order Differentiation Model for Low-Dose CT Image Processing , 2016, IEEE Access.

[47]  Vipin Tyagi,et al.  A survey of edge-preserving image denoising methods , 2016, Inf. Syst. Frontiers.

[48]  Feiniu Yuan,et al.  Remote Sensing Image Fusion Based on Adaptive IHS and Multiscale Guided Filter , 2016, IEEE Access.

[49]  Coloma Ballester,et al.  Affine Non-Local Means Image Denoising , 2017, IEEE Transactions on Image Processing.

[50]  T. Duong,et al.  Density derivative estimation , 2018 .