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arxiv: 1907.03246 · v1 · pith:ARXVTWL6new · submitted 2019-07-07 · 📡 eess.IV · cs.CV· cs.MM

An Experimental-based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging

Yan Wang , Wei Song , Giancarlo Fortino , Lizhe Qi , Wenqiang Zhang , Antonio Liotta This is my paper

Pith reviewed 2026-05-25 01:40 UTC · model grok-4.3

classification 📡 eess.IV cs.CVcs.MM
keywords underwater imagingimage enhancementimage restorationimage formation modelexperimental evaluationquality degradationocean explorationcomparative study
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The pith

Underwater image enhancement review identifies shortcomings of current methods via experiments

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper reviews image enhancement and restoration techniques developed for underwater images that suffer from absorption and scattering. It divides approaches into those based on the image formation model and those that are not, then runs a side-by-side experimental comparison on representative test images using both human judgment and quantitative metrics. From the results the authors extract the main weaknesses that persist across methods and offer concrete recommendations for next steps. The work supplies the background needed to understand why underwater images remain hard to fix and where progress is still required.

Core claim

Through an experimental comparison of state-of-the-art IFM-free and IFM-based underwater image enhancement and restoration methods, together with their parameter-estimation algorithms, the paper identifies the key shortcomings of existing techniques and formulates recommendations for future research.

What carries the argument

The underwater image formation model (IFM) that accounts for light absorption and scattering, used both to classify methods and to guide the experimental evaluation of their performance.

If this is right

  • Researchers receive a consolidated view of challenges and opportunities in underwater imaging.
  • Shortcomings of both IFM-free and IFM-based families are made explicit for targeted follow-up work.
  • Recommendations for future method development are supplied directly from the comparative results.
  • Open evaluation code allows others to reproduce and extend the analysis.
  • Both subjective and objective measures are shown to be necessary for judging method quality.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Methods that combine elements from both IFM-free and IFM-based families may address some of the identified gaps.
  • Expanding the test set to include more varied real-world ocean conditions could sharpen the shortcomings list.
  • The released code creates a shared benchmark that future papers can use for direct comparison.
  • Parameter estimation remains a bottleneck that new learning-based approaches might bypass.

Load-bearing premise

The chosen methods, parameter estimators, and test images are representative of typical underwater conditions and current best practice.

What would settle it

New test images exhibiting extreme degradations on which every reviewed method performs well would undermine the claim that key shortcomings have been correctly identified.

Figures

Figures reproduced from arXiv: 1907.03246 by Antonio Liotta, Giancarlo Fortino, Lizhe Qi, Wei Song, Wenqiang Zhang, Yan Wang.

Figure 1
Figure 1. Figure 1: shows the interaction between light, transmission medium, camera and scene. The camera receives three types of light energy in line of sight (LOS): the direct transmission light energy reflected from the scene captured (direct transmission); the light from the scene that is scattered by small particles but still reaches the camera (forward scattering); and the light coming from atmospheric light and reflec… view at source ↗
read the original abstract

Underwater images play a key role in ocean exploration, but often suffer from severe quality degradation due to light absorption and scattering in water medium. Although major breakthroughs have been made recently in the general area of image enhancement and restoration, the applicability of new methods for improving the quality of underwater images has not specifically been captured. In this paper, we review the image enhancement and restoration methods that tackle typical underwater image impairments, including some extreme degradations and distortions. Firstly, we introduce the key causes of quality reduction in underwater images, in terms of the underwater image formation model (IFM). Then, we review underwater restoration methods, considering both the IFM-free and the IFM-based approaches. Next, we present an experimental-based comparative evaluation of state-of-the-art IFM-free and IFM-based methods, considering also the prior-based parameter estimation algorithms of the IFM-based methods, using both subjective and objective analysis (the used code is freely available at https://github.com/wangyanckxx/Single-Underwater-Image-Enhancement-and-Color-Restoration). Starting from this study, we pinpoint the key shortcomings of existing methods, drawing recommendations for future research in this area. Our review of underwater image enhancement and restoration provides researchers with the necessary background to appreciate challenges and opportunities in this important field.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper reviews the causes of quality degradation in underwater images via the image formation model (IFM), surveys both IFM-free and IFM-based enhancement/restoration methods, presents an experimental comparative evaluation of selected SOTA methods (including prior-based parameter estimation for IFM-based approaches) using subjective and objective metrics on test images, identifies key shortcomings of existing methods, and provides recommendations for future research. The associated code is released publicly.

Significance. If the experimental comparison holds under representative conditions, the work supplies a useful consolidated background on underwater imaging challenges together with practical guidance on method limitations. The public code release is a clear strength supporting reproducibility of the reported comparisons.

major comments (1)
  1. [Experimental Evaluation] Experimental section (comparative evaluation): the manuscript does not state explicit selection criteria or coverage metrics for the chosen SOTA methods, parameter-estimation algorithms, or test images (e.g., number of images, water-type diversity, scene complexity, or inclusion of extreme absorption/scattering cases). This selection is load-bearing for the central claim that the study “pinpoint[s] the key shortcomings” and draws general recommendations, because unrepresentative sampling would limit the validity of those shortcomings.
minor comments (2)
  1. [Abstract] The abstract states that the review covers “some extreme degradations,” yet the experimental description does not confirm whether the test set actually contains such cases; a short clarifying sentence would remove ambiguity.
  2. [Introduction / IFM section] Notation for the IFM parameters (e.g., direct attenuation coefficient, backscattering) should be introduced once with a consistent symbol table or equation reference to aid readers unfamiliar with the model.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We appreciate the referee's detailed review and recommendation for major revision. We address the concern regarding the experimental evaluation below.

read point-by-point responses

  1. Referee: Experimental section (comparative evaluation): the manuscript does not state explicit selection criteria or coverage metrics for the chosen SOTA methods, parameter-estimation algorithms, or test images (e.g., number of images, water-type diversity, scene complexity, or inclusion of extreme absorption/scattering cases). This selection is load-bearing for the central claim that the study “pinpoint[s] the key shortcomings” and draws general recommendations, because unrepresentative sampling would limit the validity of those shortcomings.

    Authors: We agree with the referee that the selection criteria for the SOTA methods, parameter-estimation algorithms, and test images should be explicitly stated to support the generalizability of our findings on shortcomings and recommendations. In the revised manuscript, we will add a dedicated paragraph in the experimental section outlining the selection process: methods were chosen to represent both IFM-free and IFM-based categories, including recent high-impact works; parameter estimation algorithms cover common priors; test images include a diverse set from public datasets covering various water types (e.g., clear, turbid), scene complexities, and extreme cases of absorption and scattering. We will also report coverage metrics such as the total number of images evaluated. This revision will strengthen the paper without altering the core conclusions. revision: yes

Circularity Check

0 steps flagged

No circularity: survey with external experimental comparison

full rationale

The paper is a literature review that summarizes causes of underwater image degradation, classifies existing IFM-free and IFM-based methods, and reports an experimental comparison of selected SOTA algorithms on a test set. No mathematical derivation, parameter fitting, or predictive claim is present that reduces by construction to quantities defined inside the paper itself. The experimental section uses publicly released code and standard metrics; the representativeness of the chosen methods and images is an empirical scope limitation rather than a self-referential definition or fitted prediction. No self-citation chain is invoked to justify a uniqueness theorem or ansatz. The central claims (shortcomings of existing methods, recommendations) rest on the reported comparisons and external literature, not on any internal redefinition. This satisfies the default expectation that a survey paper without derivations scores 0.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As a review paper the authors introduce no new free parameters, mathematical axioms, or postulated entities; the work rests on the standard image formation model already present in the cited literature.

pith-pipeline@v0.9.0 · 5782 in / 929 out tokens · 18157 ms · 2026-05-25T01:40:41.939089+00:00 · methodology

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Reference graph

Works this paper leans on

119 extracted references · 119 canonical work pages

  1. [1]

    Sustainability Assessment of Deep Ocean Resources,

    B. C. McLellan, “Sustainability Assessment of Deep Ocean Resources,” Procedia Environ. Sci., vol. 28, pp. 502–508, Jan. 2015

    work page 2015

  2. [2]

    Computer Vision for Ocean Observing,

    H. Lu, Y. Li, and S. Serikawa, “Computer Vision for Ocean Observing,” Artif. Intell. Comput. Vis., pp. 1–16, 2017

    work page 2017

  3. [3]

    Object recognition for cell manufacturing system,

    K. Kim, J. Kim, S. Kang, J. Kim, and J. Lee, “Object recognition for cell manufacturing system,” in 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) , 2012, pp. 512–514

    work page 2012

  4. [4]

    An automated fish species classification and migration monitoring system,

    D. J. Lee, S. Redd, R. Schoenberger, X. Xu, a nd P. Zhan, “An automated fish species classification and migration monitoring system,” in The 29th Annual Conference of the IEEE Industrial Electronics Society, 2003. IECON ’03, 2003, vol. 2, pp. 1080-1085 Vol.2

    work page 2003

  5. [5]

    A Real -Time Vehicle Navigation Algorithm in Sensor Network Environments,

    C. L. P. Chen, J. Zhou, and W. Zhao, “ A Real -Time Vehicle Navigation Algorithm in Sensor Network Environments,” IEEE Trans. Intell. Transp. Syst., vol. 13, no. 4, pp. 1657–1666, Dec. 2012

    work page 2012

  6. [6]

    Improving image quality in poor visibility conditions using a physical model for contrast degradation,

    J. P. Oakley and B. L. Satherley, “Improving image quality in poor visibility conditions using a physical model for contrast degradation,” IEEE Trans. Image Process., vol. 7, no. 2, pp. 167–179, Feb. 1998

    work page 1998

  7. [7]

    Image dehazing by artificial multiple-exposure image fusion,

    A. Galdran, “Image dehazing by artificial multiple-exposure image fusion,” Signal Process., vol. 149, pp. 135–147, Aug. 2018

    work page 2018

  8. [8]

    Shallow-Water Image Enhancement Using Relative Global Histogram Stretching Based on Adaptive Parameter Acquisition,

    D. Huang, Y. Wang , W. Song, J. Sequeira, and S. Mavromatis, “Shallow-Water Image Enhancement Using Relative Global Histogram Stretching Based on Adaptive Parameter Acquisition,” in MultiMedia Modeling, 2018, pp. 453–465

    work page 2018

  9. [9]

    Clear underwater vision,

    Y. Y. Schechner and N. Karpel, “Clear underwater vision,” in Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., 2004, vol. 1, pp. I-536-I-543 Vol.1

    work page 2004

  10. [10]

    A Computer Model For Underwater Camera Systems,

    B. L. McGlamery, “A Computer Model For Underwater Camera Systems,” presented at th e Ocean Optics VI, 1980, vol. 0208, pp. 221–232

    work page 1980

  11. [11]

    Computer modeling and the design of optimal underwater imaging systems,

    J. S. Jaffe, “Computer modeling and the design of optimal underwater imaging systems,” IEEE J. Ocean. Eng., vol. 15, no. 2, pp. 101–111, Apr. 1990

    work page 1990

  12. [12]

    Recovery of underwater visibility and structure by polarization analysis,

    Y. Y. Schechner and N. Karpel, “Recovery of underwater visibility and structure by polarization analysis,” IEEE J. Ocean. Eng. , vol. 30, no. 3, pp. 570–587, Jul. 2005

    work page 2005

  13. [13]

    Single Image Haze Removal Using Dark Channel Prior,

    K. He, J. Sun, and X. Tang, “Single Image Haze Removal Using Dark Channel Prior,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 12, pp. 2341–2353, Dec. 2011

    work page 2011

  14. [14]

    Removal of water scattering,

    L. Chao and M. Wang, “Removal of water scattering,” in 2010 2nd International Conference on Computer Engineering and Technology, 2010, vol. 2, pp. V2-35-V2-39

    work page 2010

  15. [15]

    A simple and comprehensive model f or underwater image restoration,

    X. Wu and H. Li, “A simple and comprehensive model f or underwater image restoration,” in 2013 IEEE International Conference on Information and Automation (ICIA), 2013, pp. 699– 704

    work page 2013

  16. [16]

    Underwater Image Enhancement by Wavelength Compensation and Dehazing,

    J. Y. Chiang and Y. C. Chen, “Underwater Image Enhancement by Wavelength Compensation and Dehazing,” IEEE Trans. Image Process., vol. 21, no. 4, pp. 1756–1769, Apr. 2012. VOLUME XX, 2019 17

    work page 2012

  17. [17]

    A research tool for long -term and continuous analysis of fish assemblage in coral-reefs using underwater camera footage,

    B. J. Boom et al., “A research tool for long -term and continuous analysis of fish assemblage in coral-reefs using underwater camera footage,” Ecol. Inform., vol. 23, pp. 83–97, Sep. 2014

    work page 2014

  18. [18]

    Visualization and Image Enhancement for Multistatic Underwater Laser Line Scan System Using Image-Based Rendering,

    B. Ouyang, F. Dalgleish, A. Vuorenkoski, W. Britton, B. Ramos, and B. Metzger, “Visualization and Image Enhancement for Multistatic Underwater Laser Line Scan System Using Image-Based Rendering,” IEEE J. Ocean. Eng., vol. 38, no. 3, pp. 566–580, Jul. 2013

    work page 2013

  19. [19]

    Triangular -range-intensity profile spatial -correlation method for 3D super -resolution range - gated imaging,

    W. Xinwei, L. Youfu, and Z. Yan, “Triangular -range-intensity profile spatial -correlation method for 3D super -resolution range - gated imaging,” Appl. Opt., vol. 52, no. 30, pp. 7399 –7406, Oct. 2013

    work page 2013

  20. [20]

    A unified framework for image performance enhancement of extended range laser seabed survey sensors,

    F. Dalgleish, B. Ouyang, and A. Vuorenkoski, “A unified framework for image performance enhancement of extended range laser seabed survey sensors,” in 2013 IEEE International Underwater Technology Symposium (UT), 2013, pp. 1–7

    work page 2013

  21. [21]

    A novel application of range-gated underwater laser imaging system (ULIS) in near-target turbid medium,

    C. Tan, G. Seet, A. Sluzek, and D. He, “A novel application of range-gated underwater laser imaging system (ULIS) in near-target turbid medium,” Opt. Lasers Eng., vol. 43, no. 9, pp. 995–1009, Sep. 2005

    work page 2005

  22. [22]

    Regularized Image Recovery in Scattering Media,

    Y. Y. Schechner and Y. Averbuch, “Regularized Image Recovery in Scattering Media,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 9, pp. 1655–1660, Sep. 2007

    work page 2007

  23. [23]

    Adaptive histogram equalization based fusion technique for hazy underwater image enhancement,

    R. Singh and M. Biswas, “Adaptive histogram equalization based fusion technique for hazy underwater image enhancement,” in 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2016, pp. 1–5

    work page 2016

  24. [24]

    A semi-global color correction for underwater image restoration,

    C. O. Ancuti, C. Ancuti, C. De Vleeschouwer, and R. Garcia, “A semi-global color correction for underwater image restoration,” in ACM SIGGRAPH 2017 Posters on - SIGGRAPH ’17, Los Angeles, California, 2017, pp. 1–2

    work page 2017

  25. [25]

    Enhancement of Underwater Images with Statistical Model of Background Light and Optimization of Transmission Map,

    W. Son g, Y. Wang, D. Huang, A. Liotta, and C. Perra, “Enhancement of Underwater Images with Statistical Model of Background Light and Optimization of Transmission Map,” p. 17

    work page

  26. [26]

    An Advanced Visibility Restoration Technique for Underwater Images,

    S. Gautam, T. K. Gandhi, and B. K. Panigrahi, “An Advanced Visibility Restoration Technique for Underwater Images,” ICIP, p. 5

    work page

  27. [27]

    Improving color correction for underwater image surveys,

    J. W. Kaeli, H. Singh, C. Murphy, and C. Kunz, “Improving color correction for underwater image surveys,” in OCEANS’11 MTS/IEEE KONA, 2011, pp. 1–6

    work page 2011

  28. [28]

    A Survey on Underwater Image Enhancement Techniques,

    P. Sahu, N. Gupta, and N. Sharma, “A Survey on Underwater Image Enhancement Techniques,” Int. J. Comput. Appl. , vol. 87, no. 13, pp. 19–23, Feb. 2014

    work page 2014

  29. [29]

    Underwater Optical Image Processing: a Comprehensive Review,

    H. Lu, Y. Li, Y. Zhang, M. Chen, S. Serikawa, and A. automated fish species classification and migration monitoring system Kim, “Underwater Optical Image Processing: a Comprehensive Review,” Mob. Netw. Appl., vol. 22, no. 6, pp. 1204–1211, Dec. 2017

    work page 2017

  30. [30]

    A Review on Intelligence Dehazing and Color Restoration for Underwater Images,

    M. Han, Z. Lyu, T. Qiu, and M. Xu, “A Review on Intelligence Dehazing and Color Restoration for Underwater Images,” IEEE Trans. Syst. Man Cybern. Syst., pp. 1–13, 2018

    work page 2018

  31. [31]

    Hue preserving- based approach for underwater colour image enhancement,

    G. Hou, Z. Pan, B. Huang, G. Wang, and X. Luan, “Hue preserving- based approach for underwater colour image enhancement,” IET Image Process., vol. 12, no. 2, pp. 292–298, 2018

    work page 2018

  32. [32]

    Color balance and fusion for underwater image enhancement,

    C. O. Ancuti, C. Ancuti, C. D. Vleeschouwer, and P. Bekaert, “Color balance and fusion for underwater image enhancement,” IEEE Trans. Image Process., vol. PP, no. 99, pp. 1–1, 2017

    work page 2017

  33. [33]

    Enhancing the low quality images using Unsupervised Colour Correcti on Method,

    K. Iqbal, M. Odetayo, A. James, R. A. Salam, and U. O. I. P. a C. R. Talib, “Enhancing the low quality images using Unsupervised Colour Correcti on Method,” in 2010 IEEE International Conference on Systems, Man and Cybernetics, 2010, pp. 1703–1709

    work page 2010

  34. [34]

    Underwater image quality enhancement through integrated color model with Rayleigh distribution,

    A. S. A. Ghani and N. A. M. Isa, “Underwater image quality enhancement through integrated color model with Rayleigh distribution,” Appl. Soft Comput., vol. 27, pp. 219–230, Feb. 2015

    work page 2015

  35. [35]

    Single Image Dehazing by Multi-Scale Fusion,

    C. O. Ancuti and C. Ancuti, “Single Image Dehazing by Multi-Scale Fusion,” IEEE Trans. Image Process., vol. 22, no. 8, pp. 3271–3282, Aug. 2013

    work page 2013

  36. [36]

    Underwater image quality enhancement through Rayleigh -stretching and averaging image planes,

    A. S. A. Ghani and N. A. M. Isa, “Underwater image quality enhancement through Rayleigh -stretching and averaging image planes,” Int. J. Nav. Archit. Ocean Eng., vol. 6, no. 4, pp. 840–866, Dec. 2014

    work page 2014

  37. [37]

    Automatic system for improving underwater image contrast and color through re cursive adaptive histogram modification,

    A. S. Abdul Ghani and N. A. Mat Isa, “Automatic system for improving underwater image contrast and color through re cursive adaptive histogram modification,” Comput. Electron. Agric. , vol. 141, no. Supplement C, pp. 181–195, Sep. 2017

    work page 2017

  38. [38]

    Underwater image enhancement by wavelet based fusion,

    A. Khan, S. S. A. Ali, A. S. Malik, A. Anwer, and F. Meriaudeau, “Underwater image enhancement by wavelet based fusion,” in 2016 IEEE International Conference on Underwater System Technology: Theory and Applications (USYS), Penang, Malaysia, 2016, pp. 83– 88

    work page 2016

  39. [39]

    Study on Underwater Image Denoising Algorithm Based on Wavelet Transform,

    S. Jian and W. Wen, “Study on Underwater Image Denoising Algorithm Based on Wavelet Transform,” J. Phys. Conf. Ser., vol. 806, p. 012006, Feb. 2017

    work page 2017

  40. [40]

    Wavelet based perspective on variational enhancement technique for underwater imagery,

    S. Vasamsetti, N. Mittal, B. C. Neelapu, and H. K. Sardana, “Wavelet based perspective on variational enhancement technique for underwater imagery,” Ocean Eng., vol. 141, no. Supplement C, pp. 88–100, Sep. 2017

    work page 2017

  41. [41]

    Naturalness Preserved Enhancement Algorithm for Non -Uniform Illumination Images,

    S. Wang, J. Zheng, H. M. Hu, and B. Li, “Naturalness Preserved Enhancement Algorithm for Non -Uniform Illumination Images,” IEEE Trans. Image Process. , vol. 22, no. 9, pp. 3538 –3548, Sep. 2013

    work page 2013

  42. [42]

    Automatic Contrast Enhancement Technology With Saliency Preservation,

    K. Gu, G. Zhai, X. Yang, W. Zhang, and C. W. Chen, “Automatic Contrast Enhancement Technology With Saliency Preservation,” IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 9, pp. 1480– 1494, Sep. 2015

    work page 2015

  43. [43]

    WaterGAN: Unsupervised Generative Network to E nable Real - Time Color Correction of Monocular Underwater Images,

    J. Li, K. A. Skinner, R. M. Eustice, and M. Johnson -Roberson, “WaterGAN: Unsupervised Generative Network to E nable Real - Time Color Correction of Monocular Underwater Images,” IEEE Robot. Autom. Lett., vol. 3, no. 1, pp. 387–394, Jan. 2018

    work page 2018

  44. [44]

    A deep CNN method for underwater image enhancement,

    Y. Wang, J. Zhang, Y. Cao, and Z. Wang, “A deep CNN method for underwater image enhancement,” in 2017 IEEE International Conference on Image Processing (ICIP), Beijing, 2017, pp. 1382 – 1386

    work page 2017

  45. [45]

    An Underwater Image Enhancement Benchmark Dataset and Beyond,

    C. Li et al. , “An Underwater Image Enhancement Benchmark Dataset and Beyond,” ArXiv190105495 Cs, Jan. 2019

    work page 2019

  46. [46]

    Review of Video and Image Defogging Algorithms and Related Studies on Image Restoration and Enhancement,

    Y. Xu, J. Wen, L. Fei, and Z. Zhang, “Review of Video and Image Defogging Algorithms and Related Studies on Image Restoration and Enhancement,” IEEE Access, vol. 4, pp. 165–188, 2016

    work page 2016

  47. [47]

    Image enhancement by histogram transformation,

    R. Hummel, “Image enhancement by histogram transformation,” Comput. Graph. Image Process. , vol. 6, no. 2, pp. 184 –195, Apr. 1977

    work page 1977

  48. [48]

    Contrast limited adaptive histogram equalization,

    K. Zuiderveld, “Contrast limited adaptive histogram equalization,” P. S. Heckbert, Ed. San Diego, CA, USA: Academic Press Professional, Inc., 1994, pp. 474–485

    work page 1994

  49. [49]

    A Generalized Unsharp Masking Algorithm,

    Guang Deng, “A Generalized Unsharp Masking Algorithm,” IEEE Trans. Image Process., vol. 20, no. 5, pp. 1249–1261, May 2011

    work page 2011

  50. [50]

    Enhancing underwater images and videos by fusion,

    C. Ancuti, C. O. Ancuti, T. Haber, and P. Bekaert, “Enhancing underwater images and videos by fusion,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 81–88

    work page 2012

  51. [51]

    Underwater image colour constancy based on DSNMF,

    X. Liu, G. Zhong, C. Liu, and J. Dong, “Underwater image colour constancy based on DSNMF,” IET Image Process., vol. 11, no. 1, pp. 38–43, 2017

    work page 2017

  52. [52]

    Color Correction of Underwater Images for Aquatic Robot Inspection,

    L. A. Torres -Méndez and G. Dudek, “Color Correction of Underwater Images for Aquatic Robot Inspection,” in Energy Minimization Methods in Computer Vision and Pattern Recognition, 2005, pp. 60–73

    work page 2005

  53. [53]

    Underwater Image Enhancement Using An Integrated Colour Model.,

    K. Iqbal, R. Abdul Salam, M. A. Osman, and A. Z. Talib, “Underwater Image Enhancement Using An Integrated Colour Model.,” IAENG Int. J. Comput. Sci. , vol. 32, no. 2, pp. 239 –244, 2007

    work page 2007

  54. [54]

    Enhancement of low quality underwater image through integrated global and local contrast correction,

    A. S. A. Ghani and N. A. M. Isa, “Enhancement of low quality underwater image through integrated global and local contrast correction,” Appl. Soft Comput., vol. 37, no. Supplement C, pp. 332– 344, Dec. 2015

    work page 2015

  55. [55]

    A retinex-based enhancing approach for single underwater image,

    X. Fu, P. Zhuang, Y. Huang, Y. Liao, X. P. Zhang, and X. Ding, “A retinex-based enhancing approach for single underwater image,” in 2014 IEEE International Conference on Image Processing (ICIP) , 2014, pp. 4572–4576

    work page 2014

  56. [56]

    Underwater image enhancement via extended multi -scale Retinex,

    S. Zhan g, T. Wang, J. Dong, and H. Yu, “Underwater image enhancement via extended multi -scale Retinex,” Neurocomputing, vol. 245, no. Supplement C, pp. 1–9, Jul. 2017

    work page 2017

  57. [57]

    Mixture contrast limited adaptive histogram equalization for underwater image enhancement,

    M. S. Hitam, E. A. Awalludin, W. N. J. H. W. Yussof, and Z. Bachok, “Mixture contrast limited adaptive histogram equalization for underwater image enhancement,” in 2013 International VOLUME XX, 2019 18 Conference on Computer Applications Technology (ICCAT), 2013, pp. 1–5

    work page 2013

  58. [58]

    Image enhancement based on contourle t transform,

    M. H. Asmare, V. S. Asirvadam, and A. F. M. Hani, “Image enhancement based on contourle t transform,” Signal Image Video Process., vol. 9, no. 7, pp. 1679–1690, Oct. 2015

    work page 2015

  59. [59]

    Transform -based image enhancement algorithms with performance measure,

    S. S. Agaian, K. Panetta, and A. M. Grigoryan, “Transform -based image enhancement algorithms with performance measure,” IEEE Trans. Image Process., vol. 10, no. 3, pp. 367–382, Mar. 2001

    work page 2001

  60. [60]

    A Robust No -Reference, No-Parameter, Transform Domain Image Quality Metric for Evaluating the Quality of Color Images,

    K. Panetta, A. Samani, and S. Agaian, “A Robust No -Reference, No-Parameter, Transform Domain Image Quality Metric for Evaluating the Quality of Color Images,” IEEE Access, vol. 6, pp. 10979–10985, 2018

    work page 2018

  61. [61]

    Fusion -based underwater image enhancement by wavelet decomposi tion,

    Y. Wang, X. Ding, R. Wang, J. Zhang, and X. Fu, “Fusion -based underwater image enhancement by wavelet decomposi tion,” in 2017 IEEE International Conference on Industrial Technology (ICIT), 2017, pp. 1013–1018

    work page 2017

  62. [62]

    A Study of Transform Domain based Image Enhancement Techniques,

    G. Kaur and M. Kaur, “A Study of Transform Domain based Image Enhancement Techniques,” Int. J. Comput. Appl. , vol. 152, no. 9, pp. 25–29, Oct. 2016

    work page 2016

  63. [63]

    Color Image Enhancement via Combine Homomorphic Ratio and Histogram Equalization Approaches: Using Underwater Images as Illustrative Examples,

    A. M. Grigoryan and S. S. Agaian, “Color Image Enhancement via Combine Homomorphic Ratio and Histogram Equalization Approaches: Using Underwater Images as Illustrative Examples,” vol. 4, no. 5, p. 12

    work page

  64. [64]

    Underwater image denoising using adaptive wavelet subband thresholding,

    C. J. Prabhakar and P. U. P. Kumar, “Underwater image denoising using adaptive wavelet subband thresholding,” in 2010 International Conference on Signal and Image Processing, 2010, pp. 322–327

    work page 2010

  65. [65]

    When Underwater Imagery Analysis Meets Deep Learning: a Solution at the Age of Big Visual Data,

    H. Qin, X. Li, Z. Yang, and M. Shang, “When Underwater Imagery Analysis Meets Deep Learning: a Solution at the Age of Big Visual Data,” p. 5

    work page

  66. [66]

    ImageNet classification with deep convolutional neural networks,

    A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, 2012

    work page 2012

  67. [67]

    Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups,

    G. Hinton et al., “Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups,” IEEE Signal Process. Mag., vol. 29, no. 6, pp. 82–97, Nov. 2012

    work page 2012

  68. [68]

    Learning Dual Convolutional Neural Networks for Low-Level Vision,

    J. Pan et al., “Learning Dual Convolutional Neural Networks for Low-Level Vision,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , Salt Lake City, UT, 2018, pp. 3070–3079

    work page 2018

  69. [69]

    Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks,

    J. Zhang et al., “Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, 2018, pp. 2521–2529

    work page 2018

  70. [70]

    Deep Semantic Face Deblurring,

    Z. Shen, W. -S. Lai, T. Xu, J. Kautz, and M. -H. Yang, “Deep Semantic Face Deblurring,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 8260–8269

    work page 2018

  71. [71]

    Blind Image Deblurring via Deep Discriminative Priors,

    L. Li, J. Pan, W.-S. Lai, C. Gao, N. Sang, and M. -H. Yang, “Blind Image Deblurring via Deep Discriminative Priors,” Int. J. Comput. Vis., Jan. 2019

    work page 2019

  72. [72]

    Removing Rain from Single Images via a Deep Detail Network,

    X. Fu, J. Huang, D. Zeng, Y. Huang, X. Ding, and J. Paisley, “Removing Rain from Single Images via a Deep Detail Network,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 1715–1723

    work page 2017

  73. [73]

    Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising,

    K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising,” IEEE Trans. Image Process., vol. 26, no. 7, pp. 3142– 3155, Jul. 2017

    work page 2017

  74. [74]

    LLNet: A deep autoencoder approach to natural low -light image enhancement,

    K. G. Lore, A. Akintayo, and S. Sarkar, “LLNet: A deep autoencoder approach to natural low -light image enhancement,” Pattern Recognit., vol. 61, pp. 650–662, Jan. 2017

    work page 2017

  75. [75]

    LIME: Low-Light Image Enhancement via Illumination Map Estimation,

    X. Guo, Y. Li, and H. Ling, “LIME: Low-Light Image Enhancement via Illumination Map Estimation,” IEEE Trans. Image Process., vol. 26, no. 2, pp. 982–993, Feb. 2017

    work page 2017

  76. [76]

    DehazeNet: An End - to-End System for Single Image Haze Remova l,

    B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, “DehazeNet: An End - to-End System for Single Image Haze Remova l,” IEEE Trans. Image Process., vol. 25, no. 11, pp. 5187–5198, Nov. 2016

    work page 2016

  77. [77]

    Proximal Dehaze -Net: A Prior Learning - Based Deep Network for Single Image Dehazing,

    D. Yang and J. Sun, “Proximal Dehaze -Net: A Prior Learning - Based Deep Network for Single Image Dehazing,” in Computer Vision – ECCV 2018 , vol. 11211, V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss, Eds. Cham: Springer International Publishing, 2018, pp. 729–746

    work page 2018

  78. [78]

    Deep Learning Based Single Image Dehazing,

    P. L. Suarez, A. D. Sappa, B. X. Vintimilla, and R. I. Hammoud, “Deep Learning Based Single Image Dehazing,” in 2018 IEEE/CVF Conference on Computer Vision an d Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA, 2018, pp. 1250 – 12507

    work page 2018

  79. [79]

    Investigating Haze -relevant Features in A Learning Framework for Image Dehazing,

    K. Tang, J. Yang, and J. Wang, “Investigating Haze -relevant Features in A Learning Framework for Image Dehazing,” presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 2995–3000

    work page 2014

  80. [80]

    A Deep Learning Approach for Underwater Image Enhancement,

    J. Perez, A. C. Attanasio, N. Nechyporenko, and P. J. Sanz, “A Deep Learning Approach for Underwater Image Enhancement,” in Biomedical Applications Based on Natural and Artificial Computing, vol. 10338, J. M. Ferrá ndez Vicente, J. R. Álvarez - Sá nchez, F. de la Paz Ló pez, J. Toledo Moreo, and H. Adeli, Eds. Cham: Springer International Publishing, 2017...

    work page 2017

Showing first 80 references.