Underwater image processing method for fish localization and detection in submarine environment

A new underwater image preprocessing method for underwater detection is proposed.This method consists of three procedures. One is image denoising, another is image segmentation via mean-shift algorithm, and the other is log likelihood ratio test.Poisson-Gauss mixture algorithm is proposed for noise reduction.Log-Likelihood ratio test is applied for robust fish detection.Experimental results outperform the state of the art methods. Object detection is an important process in image processing, it aims to detect instances of semantic objects of a certain class in digital images and videos. Object detection has applications in many areas of computer vision such as underwater fish detection. In this paper we present a method for preprocessing and fish localization in underwater images. We are based on a Poisson-Gauss theory, because it can accurately describe the noise present in a large variety of imaging systems. In the preprocessing step we denoise and restore the raw images. These images are split into regions utilizing the mean shift algorithm. For each region, statistical estimation is done independently in order to combine regions into objects. The method is tested under different underwater conditions. Experimental results show that the proposed approach outperforms state of the art methods.

[1]  Viktor Gruev,et al.  Advances in integrated polarization image sensors , 2009, 2009 IEEE/NIH Life Science Systems and Applications Workshop.

[2]  P. C. Chang,et al.  Improving visibility depth in passive underwater imaging by use of polarization. , 2003, Applied optics.

[3]  William J. Kirkwood.  AUV Technology and Application Basics , 2008, OCEANS 2008 - MTS/IEEE Kobe Techno-Ocean.

[4]  Jiangshe Zhang,et al.  Image Denoising via Clustering-Based Sparse Representation over Wiener and Gaussian Filters , 2012, 2012 Spring Congress on Engineering and Technology.

[5]  C. C. Insaurralde,et al.  Autonomic management for the next generation of Autonomous Underwater Vehicles , 2012, 2012 IEEE/OES Autonomous Underwater Vehicles (AUV).

[6]  Sanjoy Dasgupta,et al.  Learning mixtures of Gaussians , 1999, 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039).

[7]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[8]  Sun Feifei,et al.  An Approach for Underwater Image Denoising Via Wavelet Decomposition and High-pass Filter , 2011, 2011 Fourth International Conference on Intelligent Computation Technology and Automation.

[9]  Fuyuan Peng,et al.  Laser underwater target detection based on Gabor transform , 2009, 2009 4th International Conference on Computer Science & Education.

[10]  Itthisek Nilkhamhang,et al.  A robust adaptive control algorithm for remotely operated underwater vehicle , 2013, The SICE Annual Conference 2013.

[11]  Alessandro Foi,et al.  Optimal Inversion of the Generalized Anscombe Transformation for Poisson-Gaussian Noise , 2013, IEEE Transactions on Image Processing.

[12]  Feng Liu,et al.  Two iterative algorithms for maximum likelihood esitimation of Gaussian mixture parameter , 2013, 2013 Ninth International Conference on Natural Computation (ICNC).

[13]  C.J. Prabhakar,et al.  Underwater image denoising using adaptive wavelet subband thresholding , 2010, 2010 International Conference on Signal and Image Processing.

[14]  Jean-Michel Morel,et al.  Non-Local Means Denoising , 2011, Image Process. Line.

[15]  Chinchen Chang,et al.  An Adaptive Median Filter for Image Denoising , 2008, 2008 Second International Symposium on Intelligent Information Technology Application.

[16]  Alessandro Foi,et al.  Noise estimation and removal in MR imaging: The variance-stabilization approach , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[17]  Ming Zhang,et al.  Multiresolution Bilateral Filtering for Image Denoising , 2008, IEEE Transactions on Image Processing.

[18]  Deni Bonnier,et al.  LUCIE: a Laser Underwater Camera Image Enhancer , 1993, Proceedings of OCEANS '93.

[19]  A. Nath,et al.  Image Denoising Algorithms: A Comparative Study of Different Filtration Approaches Used in Image Restoration , 2013, 2013 International Conference on Communication Systems and Network Technologies.

[20]  Shiming Wang,et al.  Underwater polarization imaging technology , 2009, 2009 Conference on Lasers & Electro Optics & The Pacific Rim Conference on Lasers and Electro-Optics.

[21]  E. B. Andersen,et al.  Information Science and Statistics , 1986 .

[22]  H. Rabbani,et al.  Wavelet Based Image Denoising with A Mixture of Gaussian Distributions with Local Parameters , 2006, Proceedings ELMAR 2006.

[23]  Bing Ouyang,et al.  Visualization and Image Enhancement for Multistatic Underwater Laser Line Scan System Using Image-Based Rendering , 2013, IEEE Journal of Oceanic Engineering.

[24]  Barry Swartz,et al.  Laser range-gated underwater imaging including polarization discrimination , 1991, Optics & Photonics.

[25]  Donna M. Kocak,et al.  The Current Art of Underwater Imaging -- With a Glimpse of the Past and Vision of the Future , 2005 .

[26]  L. Gao,et al.  Technique for False Image Correction in Second Harmonic Generation Microscopy by Modulating Laser Polarization , 2006, 2006 International Symposium on Biophotonics, Nanophotonics and Metamaterials.

[27]  Legris,et al.  3 - Les capteurs d'imagerie en robotique sous-marine : tendances actuelles et futures , 2003 .

[28]  Hugues Talbot,et al.  A primal-dual proximal splitting approach for restoring data corrupted with poisson-gaussian noise , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[29]  Jiong Wang,et al.  On the Convergence of Generalized Simultaneous Iterative Reconstruction Algorithms , 2007, IEEE Transactions on Image Processing.