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Contrast improvement on side scan sonar images using retinex based edge preserved technique

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Abstract

The complex underwater environment and sonar parameters make the captured acoustic side scan sonar imagery to suffer from depleted contrast, low brightness, speckle noise, and deteriorated contour. Though the electromagnetic waves are highly absorbed in water and sonar is exemplary considered, these issues will affect the performance of the imaging Side Scan Sonar (SSS). Hence, these images need effective enhancement to achieve a privileged visual effect. The paper proposes the Retinex based Contrast-Enhanced Edge Preserved (RCEEP) technique to enhance the low-quality SSS image. Initially, the degraded image is convolved with a smoothing filter to obtain an illumination map. After the noise suppression, the reflectance map is computed and the brightness factor is interpolated. To rid of the blurred edges, the amended unsharp mask filter is applied to obtain the sharp-contour and smoothens the speckle noise. Finally, the contrast factor is weighted with a masked image to retain the contrast-enhanced sharpened image. The qualitative and quantitative analysis is carried out on the acoustic imagery. To evaluate each of the image attributes, the considered quantitative parameters are Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Contrast Enhancement based contrast-changed Image Quality (CEIQ), Natural Scene Statistics (NSS), and Perceptual Sharpness Index (PSI). It is observed that the proposed RCEEP methodology enhances even the features in the dark region and outperforms the other state-of-the-art enhancement techniques.

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Abbreviations

HE:

Histogram Equalization

BBHE:

Brightness-preserving Bi-Histogram Equalization

DSIHE:

Dualistic SubImage Histogram Equalization

MMBEBHE:

Minimum Mean Brightness Error Bi-Histogram Equalization

IBBHE:

Iterative of Brightness Bi-Histogram Equalization

AHE:

Adaptive Histogram Equalization

POSHE:

Partially Overlapped Subblock Histogram Equalization

CLAHE:

Contrast-Limited Adaptive Histogram Equalization

RMSHE:

Recursive Mean-Separate Histogram Equalization

BBPHE:

Background Brightness-Preserving Histogram Equalization

GCCHE:

Gain-Controllable Clipped Histogram Equalization

RSIHE:

Recursive SubImage Histogram Equalization

DHE:

Dynamic Histogram Equalization

BPDHE:

Brightness-Preserving Dynamic Histogram Equalization

EDSHE:

Entropy-based Dynamic SubHistogram Equalization

BHEPL:

Bi-Histogram Equalization with a Plateau Limit

MMSICHE:

Median-Mean based SubImage-Clipped Histogram Equalization

ESIHE:

Exposure-based SubImage Histogram Equalization

AMHE:

Adaptively Modified Histogram Equalization

WHE:

Weighted Histogram Equalization

CegaHE:

Gap adjustment for Histogram Equalization

SSR:

Single-Scale Retinex

MSR:

Multi-Scale Retinex

MSRCR:

Multi-Scale Retinex with Color Restoration

KBR:

Kernel-Based Retinex

MSRCP:

Multi-Scale Retinex with Chromaticity Preservation

SRIE:

Simultaneous Reflectivity and Illumination Estimation

NPE:

Naturalness Preserved Enhancement

LIME:

Low-light Image Enhancement via Illumination Map estimation

SSDA-LLNet:

Stacked Sparse Denoising Autoencoder – Low Light Network

LLCNN:

Convolutional Neural Network for Low Light image enhancement

GLAD-Net:

GLobal illumination-Aware and Detail-preserving Network

MSR-Net:

Multi-Scale Retinex based Convolutional Neural Network

LLIE-Net:

Denoising Net and Low Light Image Enhancement net

References

  • Andreatos A, Leros A (2021) Contour Extraction Based on Adaptive Thresholding in Sonar Images. Information 12(9):354

    Article  Google Scholar 

  • Dondurur D (2018) Acquisition and Processing of Marine Seismic Data: Elsevier 2018:1–35. https://doi.org/10.1016/B978-0-12-811490-2.00001-3

    Article  Google Scholar 

  • Fang Y, Ma K, Wang Z, Lin W, Fang Z, Zhai G (2014) No-reference quality assessment of contrast-distorted images based on natural scene statistics. IEEE Signal Process Lett 22(7):838–842

    Google Scholar 

  • Feichtenhofer C, Fassold H, Schallauer P (2013) A perceptual image sharpness metric based on local edge gradient analysis. IEEE Signal Process Lett 20(4):379–382

    Article  Google Scholar 

  • Guo X, Li Y, Ling H (2016) LIME: Low-light image enhancement via illumination map estimation. IEEE Trans Image Process 26(2):982–993

    Article  Google Scholar 

  • Hao, P., Wang, S., Li, S., & Yang, M. (2019, November). Low-light image enhancement based on retinex and saliency theories. In 2019 Chinese Automation Congress (CAC) (pp. 2594–2597). IEEE.

  • Jeon G (2017) Computational intelligence approach for medical images by suppressing noise. J Ambient Intellig Humanized Comput. https://doi.org/10.1007/s12652-017-0627-9

    Article  Google Scholar 

  • Land EH, McCann JJ (1971) Lightness and retinex theory. Josa 61(1):1–11

    Article  Google Scholar 

  • Lakshmi, M. D., Raj, M. V., & Murugan, S. S. (2019a). Feature matching and assessment of similarity rate on geometrically distorted side scan sonar images. In 2019a TEQIP III Sponsored International Conference on Microwave Integrated Circuits, Photonics and Wireless Networks (IMICPW) (pp. 208–212). IEEE.

  • Lakshmi, M. D., Murugan, S. S., Padmapriya, N., & Somasekar, M. (2019b, December). Texture Analysis on Side Scan Sonar images using EMD, XCS-LBP and Statistical Co-occurrence. In 2019b International Symposium on Ocean Technology (SYMPOL) (pp. 91–97). IEEE.

  • Lakshmi MD, Murugan SS (2020) Keypoint-based mapping analysis on transformed Side Scan Sonar images. Multimedia Tools and Applications 79(35):26703–26733

    Article  Google Scholar 

  • Lakshmi MD, Murugan SS (2021) Modified restoration technique on shallow underwater imagery for improved visual perception. Curr Sci 121(1):103–108

    Article  Google Scholar 

  • Li M, Liu J, Yang W, Sun X, Guo Z (2018) Structure-revealing low-light image enhancement via robust retinex model. IEEE Trans Image Process 27(6):2828–2841

    Article  Google Scholar 

  • Malik R, Dhir R, Mittal SK (2019) Remote sensing and landsat image enhancement using multiobjective PSO based local detail enhancement. J Ambient Intell Humaniz Comput 10(9):3563–3571

    Article  Google Scholar 

  • Moghimi MK, Mohanna F (2021) Real-time underwater image enhancement: a systematic review. J Real-Time Image Proc 18:1509–1525

    Article  Google Scholar 

  • Murugan, S. S., & Natarajan, V. (2010, January). Performance analysis of signal to noise ratio and bit error rate for multiuser using passive time reversal technique in underwater communication. In 2010 International Conference on Wireless Communication and Sensor Computing (ICWCSC) (pp. 1–4). IEEE.

  • Muthuraman, D. L., & Santhanam, S. M. (2021). Visibility improvement of underwater turbid image using hybrid restoration network with weighted filter. Multidimensional Systems and Signal Processing, 1–26.

  • Mulyantini A, Choi HK (2016) Color image enhancement using a Retinex algorithm with bilateral filtering for images with poor illumination. J Korea Multimedia Soc 19(2):233–239

    Article  Google Scholar 

  • Narvekar ND, Karam LJ (2011) A no-reference image blur metric based on the cumulative probability of blur detection (CPBD). IEEE Trans Image Process 20(9):2678–2683

    Article  Google Scholar 

  • Park S, Yu S, Moon B, Ko S, Paik J (2017) Low-light image enhancement using variational optimization-based retinex model. IEEE Trans Consum Electron 63(2):178–184

    Article  Google Scholar 

  • Priyadharsini R, Sharmila TS, Rajendran V (2018) A wavelet transform based contrast enhancement method for underwater acoustic images. Multidimension Syst Signal Process 29(4):1845–1859

    Article  Google Scholar 

  • Ren Y, Ying Z, Li TH, Li G (2018) LECARM: Low-light image enhancement using the camera response model. IEEE Trans Circuits Syst Video Technol 29(4):968–981

    Article  Google Scholar 

  • Sheikh HR, Bovik AC (2005) A visual information fidelity approach to video quality assessment. In the First International Workshop on Video Processing and Quality Metrics for Consumer Electronics 7:975–989

    Google Scholar 

  • Sun, L., & Guo, H. (2021) Comparison of Contrast Enhancement Methods for Underwater Target Sonar Images. In Advances in Wireless Communications and Applications (pp. 225–232). Springer Singapore.

  • Tanaka, H., Waizumi, Y., & Kasezawa, T. (2017). Retinex-based signal enhancement for image dark regions. In 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (pp. 205–209). IEEE.

  • Veni, S. K., Murugan, S. S., & Natarajan, V. (2011) Modified LMS adaptive algorithm for detection of underwater acoustic signals against ambient noise in shallow water of Indian Sea. In 2011 International Conference on Recent Trends in Information Technology (ICRTIT) (pp. 901–905). IEEE.

  • Wang, M. R., & Jiang, S. Q. (2015) Image enhancement algorithm combining multi-scale Retinex and bilateral filter. In 2015 International Conference on Automation, Mechanical Control and Computational Engineering. Atlantis Press.

  • Wang H, Gao N, Xiao Y, Tang Y (2020a) Image feature extraction based on improved FCN for UUV side-scan sonar. Marine Geophysical Research 41(4):1–17

    Google Scholar 

  • Wang W, Wu X, Yuan X, Gao Z (2020b) An experiment-based review of low-light image enhancement methods. IEEE Access 8:87884–87917

    Article  Google Scholar 

  • Wu, Z., Yang, F., & Tang, Y. (2021). Side-scan Sonar and Sub-bottom Profiler Surveying. In High-resolution Seafloor Survey and Applications (pp. 95–122). Springer Singapore

  • Ye X, Yang H, Li C, Jia Y, Li P (2019) A Gray Scale Correction Method for Side-Scan Sonar Images Based on Retinex. Remote Sensing 11(11):1281

    Article  Google Scholar 

  • Yan, J., Li, J., & Fu, X. (2019). No-reference quality assessment of contrast-distorted images using contrast enhancement. arXiv preprint

  • Zhang, Y., Huang, W., Bi, W., & Gao, G. (2016, August). Colorful image enhancement algorithm based on guided filter and Retinex. In 2016 IEEE International Conference on Signal and Image Processing (ICSIP) (pp. 33–36). IEEE.

  • Zhang, Y., Li, H., Zhu, J., Zhou, L., & Chen, B. (2021, July). Contrast Study of Side Scan Sonar Image Enhancement Methods. In 2021 OES China Ocean Acoustics (COA) (pp. 995–999). IEEE.

  • Zhang S, Tang GJ, Liu XH, Luo SH, Wang DD (2018) Retinex based low-light image enhancement using guided filtering and variational framework. Optoelectron Lett 14(2):156–160

    Article  Google Scholar 

Download references

Acknowledgements

The authors wish to thank C-MAX for supporting us by providing their Side Scan Sonar data. They also wish to extend their sincere thanks to Mr.Hugh Frater, C-MAX. This work is a part of a project funded by the Department of Science and Technology (DST) under SSTP. Grant No: DST/SSTP/Tamilnadu/102/2017-18.

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Authors and Affiliations

  1. Underwater Acoustic Research Laboratory, Department of Electronics and Communication Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, 603110, Tamilnadu, India

    Dhana Lakshmi Muthuraman & Sakthivel Murugan Santhanam

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  1. Dhana Lakshmi Muthuraman
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  2. Sakthivel Murugan Santhanam
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Correspondence to Sakthivel Murugan Santhanam.

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Muthuraman, D.L., Santhanam, S.M. Contrast improvement on side scan sonar images using retinex based edge preserved technique. Mar Geophys Res 43, 17 (2022). https://doi.org/10.1007/s11001-022-09478-w

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