Login Register Cart 0
Generic placeholder image

Current Signal Transduction Therapy

Editor-in-Chief

ISSN (Print): 1574-3624
ISSN (Online): 2212-389X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Underwater Optical Image Coding for Marine Health Monitoring Based on DCT

Author(s): Mohammad Kazem Moghimi and Farahnaz Mohanna*

Volume 16, Issue 1, 2021

Published on: 08 November, 2019

Page: [23 - 37] Pages: 15

DOI: 10.2174/0929867326666191108152736

Abstract

Introduction: Optical imaging in the underwater environment to monitor marine objects is now a hot topic of research which can be used for environmental healthcare systems through the underwater ecosystem. Among different areas of research, image coding techniques are widely applied to compress data for reliable communications. One of the challenges faced during the underwater communications is having a low bit rate in acoustic links, particularly while doing imaging in deep waters (in this condition, light needed for imaging is provided by a power supply).

Materials and Methods: Two Dimensional-Discrete Cosine Transform (2D-DCT) is the main technique that we want to use for image compression, to test two different patch sizes in 2D-DCT to study the patch size effect on the quality of compression, execution time and preservation ability of high-frequency information in edges.

Results: The results clearly show that a larger patch size can always be better in terms of computational complexity, quality of coded images and also edge preservation when we use DCT for the compression process.

Discussion and Conclusion: Although this research approves the approach of JPEG codec once again for using the largest sub-image block in image compression (in terms of similarity and complexity), however, the use of an edge preservation factor is a new finding for our research. On the other hand, using the largest patch size is not a general approach for all image processing applications, because some studies have shown that smaller patch may be more effective for some other applications.

Keywords: Marine health, underwater optical imaging, image compression, DCT, fast fourier transform, health monitoring.

[1]
Lu H, Li Y, Zhang Y, Chen M, Serikawa S, Kim H. Underwater optical image processing: A comprehensive review. Mob Netw Appl 2017; 22(6): 1204-11.
[http://dx.doi.org/10.1007/s11036-017-0863-4]
[2]
Lu H, Li Y, Serikawa S. Computer vision for ocean observing. Artificial Intelligence and Computer Vision 2017; pp. 1-16.
[http://dx.doi.org/10.1007/978-3-319-46245-5]
[3]
Xu R. Particle characterization: Light scattering methods. Springer Science & Business Media 2001; Vol. 13.
[4]
Christie SM, Kvasnik F. Contrast enhancement of underwater images with coherent optical image processors. Appl Opt 1996; 35(5): 817-25.
[http://dx.doi.org/10.1364/AO.35.000817] [PMID: 21069075]
[5]
Jaffe JS, Moore KD, McLean J, Strand MP. Underwater optical imaging: Status and prospects. Oceanography (Wash DC) 2001; 14(3): 66-76.
[http://dx.doi.org/10.5670/oceanog.2001.24]
[6]
Caimi FM, Kocak DM, Dalgleish F, Watson J. Underwater imaging and optics: Recent advances oceans. IEEE 2008; pp. 1-9.
[7]
Hitam MS, Awalludin EA, Yussof WNJHW, Bachok Z. Mixture contrast limited adaptive histogram equalization for underwater image enhancement. Computer Applications Technology (ICCAT) 1-5.
[http://dx.doi.org/10.1109/ICCAT.2013.6522017]
[8]
Kocak DM, Dalgleish FR, Caimi FM, Schechner YY. A focus on recent developments and trends in underwater imaging. Mar Technol Soc J 2008; 42(1): 52-67.
[http://dx.doi.org/10.4031/002533208786861209]
[9]
Kocak DM, Caimi FM. The current art of underwater imaging–with a glimpse of the past and vision of the future. Mar Technol Soc J 2005; 39(3): 5-26.
[http://dx.doi.org/10.4031/002533205787442576]
[10]
Bouchette G, Church P, Mcfee JE, Adler A. Imaging of compact objects buried in underwater sediments using electrical impedance tomography. IEEE Trans Geosci Remote Sens 2014; 52(2): 1407-17.
[http://dx.doi.org/10.1109/TGRS.2013.2250982]
[11]
Torres-Méndez LA, Dudek G. Color correction of underwater images for aquatic robot inspection. International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. 60-73.
[http://dx.doi.org/10.1007/11585978_5]
[12]
Georgiades C, German A, Hogue A, et al. AQUA: An aquatic walking robot. Intelligent Robots and Systems 3525-1.
[http://dx.doi.org/10.1109/IROS.2004.1389962]
[13]
Tran QD, Jang GW, Kwon HS, et al. Shape optimization of acoustic lenses for underwater imaging. J Mech Sci Technol 2016; 30(10): 4633-44.
[http://dx.doi.org/10.1007/s12206-016-0934-7]
[14]
Schechner YY, Averbuch Y. Regularized image recovery in scattering media. IEEE Trans Pattern Anal Mach Intell 2007; 29(9): 1655-60.
[http://dx.doi.org/10.1109/TPAMI.2007.1141] [PMID: 17627052]
[15]
Murez Z, Treibitz T, Ramamoorthi R, Kriegman DJ. Photometric stereo in a scattering medium. IEEE Trans Pattern Anal Mach Intell 2017; 39(9): 1880-91.
[http://dx.doi.org/10.1109/TPAMI.2016.2613862] [PMID: 28114056]
[16]
Treibitz T, Schechner YY. Turbid scene enhancement using multi-directional illumination fusion. IEEE Trans Image Process 2012; 21(11): 4662-7.
[http://dx.doi.org/10.1109/TIP.2012.2208978] [PMID: 22829404]
[17]
Roser M, Dunbabin M, Geiger A. Simultaneous underwater visibility assessment, enhancement and improved stereo. Robotics and Automation (ICRA) 3840-7.
[http://dx.doi.org/10.1109/ICRA.2014.6907416]
[18]
Mori K, Ogasawara H, Nakamura T, Tsuchiya T, Endoh N. Design and convergence performance analysis of aspherical acoustic lens applied to ambient noise imaging in actual ocean experiment. Jpn J Appl Phys 2011; 50(7S)07HG09
[http://dx.doi.org/10.7567/JJAP.50.07HG09]
[19]
Ghani ASA, Isa NAM. Underwater image quality enhancement through Rayleigh-stretching and averaging image planes. Int J Nav Archit Ocean Eng 2014; 6(4): 840-66.
[http://dx.doi.org/10.2478/IJNAOE-2013-0217]
[20]
Ghani ASA, Isa NAM. Underwater image quality enhancement through integrated color model with Rayleigh distribution. Appl Soft Comput 2015; 27: 219-30.
[http://dx.doi.org/10.1016/j.asoc.2014.11.020]
[21]
Ji T, Wang G. An approach to underwater image enhancement based on image structural decomposition. J Ocean Univ China 2015; 14(2): 255-60.
[http://dx.doi.org/10.1007/s11802-015-2426-2]
[22]
Ghani ASA, Nasir AFA, Tarmizi WFW. Integration of enhanced background filtering and wavelet fusion for high visibility and detection rate of deep sea underwater image of underwater vehicle. Information and Communication Technology (ICoIC7) 1-6.
[http://dx.doi.org/10.1109/ICoICT.2017.8074678]
[23]
Wang N, Zheng H, Zheng B. Underwater image restoration via maximum attenuation identification. IEEE Access 20175: 18941-52.
[http://dx.doi.org/10.1109/ACCESS.2017.2753796]
[24]
Wang Y, Liu H, Chau LP. Single underwater image restoration using adaptive attenuation curve prior. IEEE Transactions on Circuits and Systems I: Regular papers 2017.
[25]
Peng YT, Cosman PC. Underwater image restoration based on image blurriness and light absorption. IEEE Trans Image Process 2017; 26(4): 1579-94.
[http://dx.doi.org/10.1109/TIP.2017.2663846] [PMID: 28182556]
[26]
Hu H, Zhao L, Li X, Wang H, Liu T. Underwater image recovery under the non-uniform optical field based on polarimetric imaging. IEEE Photonics J 2018; 10(1): 1-9.
[http://dx.doi.org/10.1109/JPHOT.2018.2791517]
[27]
Lu H, Li Y, Xu X, et al. Underwater image enhancement method using weighted guided trigonometric filtering and artificial light correction. J Vis Commun Image Represent 2016; 38: 504-16.
[http://dx.doi.org/10.1016/j.jvcir.2016.03.029]
[28]
Li JH, Kang HJ, Park GH, Ki HS, Suh JH. Sonar image processing based underwater localization method and its experimental studies. OCEANS–Anchorage 2017; pp. 1-5.
[29]
Li J, Skinner KA, Eustice RM, Johnson-Roberson M. WaterGAN: Unsupervised generative network to enable real-time color correction of monocular underwater images. IEEE Robot Autom Lett 2018; 3(1): 387-94.
[30]
Chen Z, Zhang Z, Dai F, Bu Y, Wang H. Monocular vision-based underwater object detection. Sensors (Basel) 2017; 17(8): 1784.
[http://dx.doi.org/10.3390/s17081784] [PMID: 28771194]
[31]
Wang N, Zheng B, Zheng H, Yu Z. Feeble object detection of underwater images through LSR with delay loop. Opt Express 2017; 25(19): 22490-8.
[http://dx.doi.org/10.1364/OE.25.022490] [PMID: 29041558]
[32]
Fandos R, Zoubir AM. Optimal feature set for automatic detection and classification of underwater objects in SAS images. IEEE J Sel Top Signal Process 2011; 5(3): 454-68.
[http://dx.doi.org/10.1109/JSTSP.2010.2093868]
[33]
Jay S, Guillaume M, Blanc-Talon J. Underwater target detection with hyperspectral data: Solutions for both known and unknown water quality. IEEE J Sel Top Appl Earth Obs Remote Sens 2012; 5(4): 1213-21.
[http://dx.doi.org/10.1109/JSTARS.2012.2185488]
[34]
Che Chuang M, Williams K, Neng Hwang J. A feature learning and object recognition framework for underwater fish images. IEEE Trans Image Process 2016; 25(4): 1862-72.
[PMID: 26930683]
[35]
Yang M, Sowmya A. An underwater color image quality evaluation metric. IEEE Trans Image Process 2015; 24(12): 6062-71.
[http://dx.doi.org/10.1109/TIP.2015.2491020] [PMID: 26513783]
[36]
Boudhane M, Nsiri B. Underwater image processing method for fish localization and detection in submarine environment. J Vis Commun Image Represent 2016; 39: 226-38.
[http://dx.doi.org/10.1016/j.jvcir.2016.05.017]
[37]
Yi Li C, Chang Guo J. Min Cong R, Wei Pang Y, Wang B. Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior. IEEE Trans Image Process 2016; 25(12): 5664-77.
[http://dx.doi.org/10.1109/TIP.2016.2612882] [PMID: 28113974]
[38]
Ghani ASA, Aris RSNAR, Zain MLM. Unsupervised contrast correction for underwater image quality enhancement through integrated-intensity stretched-Rayleigh histograms. J Telecommun Electron Comput Eng 2016; 8(3): 1-7.
[39]
Ancuti CO, Ancuti C. Single image dehazing by multi-scale fusion. IEEE Trans Image Process 2013; 22(8): 3271-82.
[http://dx.doi.org/10.1109/TIP.2013.2262284] [PMID: 23674449]
[40]
He K, Sun J, Tang X. Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 2011; 33(12): 2341-53.
[http://dx.doi.org/10.1109/TPAMI.2010.168] [PMID: 20820075]
[41]
Chiang JY, Chen YC. Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans Image Process 2012; 21(4): 1756-69.
[http://dx.doi.org/10.1109/TIP.2011.2179666] [PMID: 22180510]
[42]
Lu H, Li Y, Zhang L, Serikawa S. Contrast enhancement for images in turbid water. J Opt Soc Am A Opt Image Sci Vis 2015; 32(5): 886-93.
[http://dx.doi.org/10.1364/JOSAA.32.000886] [PMID: 26366913]
[43]
Bekaert P, Haber T, Ancuti CO, Ancuti C. Enhancing underwater images and videos by fusion. IEEE Conference on Computer Vision and Pattern Recognition 2012.
[44]
Seemakurthy K, Rajagopalan AN. Deskewing of underwater images. IEEE Trans Image Process 2015; 24(3): 1046-59.
[http://dx.doi.org/10.1109/TIP.2015.2395814] [PMID: 25622317]
[45]
Iqbal K, Salam RA, Osman A, Talib AZ. Underwater image enhancement using an integrated colour model. IAENG International Journal of Computer Science 2007.
[46]
AbuNaser A, Doush IA, Mansour N, Alshattnawi S. Underwater image enhancement using particle swarm optimization. Journal of Intelligent Systems 2015; 24(1): 99-115.
[http://dx.doi.org/10.1515/jisys-2014-0012]
[47]
Ancuti CO, Ancuti C, De Vleeschouwer C, Bekaert P. Color balance and fusion for underwater image enhancement. IEEE Trans Image Process 2018; 27(1): 379-93.
[http://dx.doi.org/10.1109/TIP.2017.2759252] [PMID: 28981416]
[48]
Petit F, Capelle-Laize AS, Carre P. Underwater image enhancement by attenuation inversion with quaternions. Acoustics, Speech and Signal Processing 2009; pp. 1177-80.
[49]
Hurtós N, Ribas D, Cufí X, Petillot Y, Salvi J. Fourier‐based registration for robust forward‐looking sonar mosaicing in low‐visibility underwater environments. J Field Robot 2015; 32(1): 123-51.
[http://dx.doi.org/10.1002/rob.21516]
[50]
Qiao X, Bao J, Zhang H, Zeng L, Li D. Underwater image quality enhancement of sea cucumbers based on improved histogram equalization and wavelet transform. Inf Process Agric 2017; 4(3): 206-13.
[http://dx.doi.org/10.1016/j.inpa.2017.06.001]
[51]
Badgujar PN, Singh JK. Underwater image enhancement using generalized histogram equalization, discrete wavelet transform and KL-transform. Int J Innov Res Sci Eng Technol 2017; 6(6): 11834-40.
[52]
Ghani ASA, Isa NAM. Automatic system for improving underwater image contrast and color through recursive adaptive histogram modification. Comput Electron Agric 2017; 141: 181-95.
[http://dx.doi.org/10.1016/j.compag.2017.07.021]
[53]
Althaf SK, Shaik MA. A study on histogram equalization techniques for underwater image enhancement. Int J Sci Res Comput Sci Eng Inf Technol 2017; p. 2.
[54]
Zhang S, Wang T, Dong J, Yu H. Underwater image enhancement via extended multi scale Retinex. Neurocomputing 2017; 245: 1-9.
[http://dx.doi.org/10.1016/j.neucom.2017.03.029]
[55]
Mercado MA, Ishii K, Ahn J. Deep-sea image enhancement using multi-scale retinex with reverse color loss for autonomous underwater vehicles. OCEANS-Anchorage 2017; 1-6.

Rights & Permissions Print Cite
Article Metrics
4
1
© 2024 Bentham Science Publishers | Privacy Policy