More Web Proxy on the site http://driver.im/

research-article

A novel biologically-inspired method for underwater image enhancement

Authors:

Guangyuan Wang,

Xianping FuAuthors Info & Claims

Volume 104, Issue C

https://doi.org/10.1016/j.image.2022.116670

Published: 01 May 2022 Publication History

Abstract

Underwater images are usually characterized by color distortion, blurry, and severe noise, because light is severely scattered and absorbed when traveling in the water. In this paper, we propose a novel method motivated by the astonishing capability of the biological vision to address the low visibility of the real-world underwater images. Firstly, we simply imitate the color constancy mechanism in photoreceptors and horizontal cells (HCs) to correct the color distortion. In particular, HCs modulation provides a global color correction with gain control, in which light wavelength-dependent absorption is taken into account. Then, to solve the problems of blurry and noise, we introduce a straightforward and effective two-pathway dehazing method. The core idea is to decompose the color corrected image into structure-pathway and texture-pathway, corresponding to the Magnocellular (M-) and Parvocellular (P-) pathway in the early visual system. In the structure-pathway, we design an innovative biological normalization model to adjust the dynamic range of luminance by integrating the bright and dark regions. By using this approach, the proposed method leads to significant improvement in the contrast degradation of underwater images. Additionally, the detail preservation and noise suppression are implemented on the textural information. Finally, we merge the outputs of structure and texture pathways to reconstruct the enhanced underwater image. Both qualitative and quantitative evaluations show that the proposed biologically-inspired method achieves better visual quality, when compared with several related methods.

References

[1]

Ancuti C., Ancuti C.O., Haber T., Bekaert P., Enhancing underwater images and videos by fusion, IEEE Conf. Comput. Vis. Pattern Recognit (2012) 81–88.

[2]

Mi Z., Li Y., Wang Y., Fu X., Multi-purpose oriented real-world underwater image enhancement, IEEE Access (2020) 112957–112968.

[3]

Schechner Y.Y., Averbuch Y., Regularized image recovery in scattering media, IEEE Trans. Pattern Anal. Mach. Intell 29 (9) (2007) 1655–1660.

[4]

Fattal R., Single image dehazing, ACM Trans. Graph 27 (3) (2008).

[5]

He K., Sun J., Tang X., Single image haze removal using dark channel prior, IEEE Trans. Pattern Anal. Mach. Intell. 33 (12) (2011) 2341–2353.

Digital Library

[6]

Yuan F., Zhan L., Pan P., Cheng E., Low bit-rate compression of underwater image based on human visual system, Signal Process. Image Commun. 91 (1) (2021).

[7]

S. Fang, Y. Yue, L. He, K. Du, Y. Tian, T. Huang, Image saliency analysis based on retina simulation, in: IEEE Third International Conference on Multimedia Big Data, 2017, pp. 142–145.

[8]

Zhang X., Gao S., Li R., Du X., Li C., Li Y., A retinal mechanism inspired color constancy model, IEEE Trans. Image Process 25 (3) (2016) 1219–1232.

[9]

Qiu J., Xu H., Ye Z., Color constancy by reweighting image feature maps, IEEE Trans. Image Process. 29 (2020) 5711–5721.

[10]

Wang Y., Wang H., Yin C., Ming D., Biologically inspired image enhancement based on Retinex, Neurocomputing 177 (2016) 373–384.

Digital Library

[11]

Xiang X., Liu L., Que L., et al., A biological retina inspired tone mapping processor for high-speed and energy-efficient image enhancement, Sensors 20 (19) (2020).

[12]

Gu K., Zhai G., Yang X., Zhang W., Using free energy principle for blind image quality assessment, IEEE Trans. Multim. 17 (1) (2014) 50–63.

[13]

Gollish T., Meister M., Eye smarter than scientists believed: Neural computations in circuits of the retina, Neuron 65 (2) (2010) 150–164.

[14]

Lebensohn J.E., Physiology of the retina and the visual pathway, Am. J. Ophthalmol. 50 (3) (1960) 514–515.

[15]

Kamermans M., Spekreijse H., Spectral behavior of cone-driven horizontal cells in Teleost retina, Prog. Retinal Eye Res. 14 (1) (1995) 313–360.

[16]

Joselevitch C., Human retinal circuitry and physiology, Psychol. Neurosci. 1 (2) (2008) 141–165.

[17]

Vanleeuwen M., Fahrenfort I., Sjoerdsma T., Numan R., Kamermans M., Lateral gain control in the outer retina leads to potentiation of center responses of retinal neurons, J. Neurosci. 29 (19) (2009) 6358–6366.

[18]

Peter H., Schiller I., Parallel information processing channels created in the retina, Proc. Nat. Acad. Sci. USA 107 (40) (2010) 17087–17094.

[19]

Kaplan E., The M, P, and K pathways of the primate visual system, Proc. Nat. Acad. Sci. USA (2004) 481–493.

[20]

Mante V., Frazor R.A., Bonin V., Geisler W.S., Carandini M., Independence of luminance and contrast in natural scenes and in the early visual system, Nat. Neuroence 8 (12) (2005) 1690–1697.

[21]

Carandini M., Heeger D.J., Normalization as a canonical neural computation, Nat. Rev. Neurosci. 13 (2012) 51–62.

[22]

Angelucci A., Shushruth S., Beyond the classical receptive field: Surround modulation in primary visual cortex, New Vis. Neurosci. (2013) 425–444.

[23]

Li C.Y., He Z.J., Effects of patterned backgrounds on responses of lateral geniculate neurons in cat, Exp. Brain Res. 67 (1) (1987) 16–26.

[24]

Liu Y., Wang A., Zhou H., Jia P., Single nighttime image dehazing based on image decomposition, Signal Process (2021).

[25]

Liang Z., Wang Y., Ding X., Mi Z., Fu X., Single underwater image enhancement by attenuation map guided color correction and detail preserved dehazing, Neurocomputing (2020).

[26]

Pizer S.M., Amburn E.P., Austin J.D., et al., Adaptive histogram equalization and its variations, Comput. Vis. Graph. Image Process 39 (3) (1987) 355–368.

Digital Library

[27]

Zuiderveld K., Contrast limited adaptive histogram equalization, Graph. Gems (1994) 474–485.

[28]

Deng G., A generalized unsharp masking algorithm, IEEE Trans. Image Process. 20 (5) (2011) 1249–1261.

[29]

Fu X., Liao Y., Zeng D., A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation, IEEE Trans. Image Process. 24 (12) (2015) 4965–4977.

Digital Library

[30]

Gu K., Zhai G., Yang X., Zhang W., Chen C.W., Automatic contrast enhancement technology with saliency preservation, IEEE Trans. Circuits Syst. Video Technol. 25 (9) (2015) 1480–1494.

Digital Library

[31]

X. Fu, Z. Fan, M. Ling, Y. Huang, X. Ding, Two-step approach for single underwater image enhancement, in: Int. Symp. Intell. Signal Process. Commun. Syst., ISPACS, 2017, pp. 789–794.

[32]

Ancuti C.O., Ancuti C., Vleeschouwer C.D., Bekaert P., Color balance and fusion for underwater image enhancement, IEEE Trans. Image Process 27 (1) (2018) 379–393.

[33]

Ke G., Tao D., Qiao J.F., Lin W., Learning a no-reference quality assessment model of enhanced images with big data, IEEE Trans. Neural Netw. Learn. Syst. 29 (4) (2018) 1301–1313.

[34]

Zhang W., Dong L., Zhang T., Xu W., Enhancing underwater image via color correction and bi-interval contrast enhancement, Signal Process. Image Commun. 90 (2021).

[35]

Chiang J., Chen Y., Underwater image enhancement by wavelength compensation and dehazing, IEEE Trans. Image Process 21 (4) (2012) 1756–1769.

Digital Library

[36]

Galdran A., Pardo D., Picón A., Alvarez-Gila A., Automatic Red-channel underwater image restoration, J. Vis. Commun. Imag. Represent 26 (2015) 132–145.

[37]

Peng Y.-T., Cao K., Cosman P.C., Generalization of the dark channel prior for single image restoration, IEEE Trans. Image Process 27 (6) (2018) 2856–2868.

[38]

Peng Y.-T., Cosman P.C., Underwater image restoration based on image blurriness and light absorption, IEEE Trans. Image Process 26 (4) (2017) 1579–1594.

Digital Library

[39]

Li J., Skinner K.A., Eustice R.M., Johnson-Roberson M., WaterGAN: Unsupervised generative network to enable real-time color correction of monocular underwater images, IEEE Robot. Autom. Lett. 3 (1) (2018) 387–394.

[40]

C. Fabbri, M.J. Islam, J. Sattar, Enhancing underwater imagery using generative adversarial networks, in: IEEE Int. Conf. Robot. Autom, ICRA, 2018, pp. 7159–7165.

[41]

Islam M.J., Xia Y., Sattar J., Fast underwater image enhancement for improved visual perception, IEEE Robot. Autom. Lett. 5 (2) (2020) 3227–3234.

[42]

Fu X., Cao X., Underwater image enhancement with global-local networks and compressed-histogram equalization, Signal Process. Image Commun. (2020).

[43]

Yang M., Hu K., Du Y., Wei Z., Sheng Z., Hu J., Underwater image enhancement based on conditional generative adversarial network, Signal Process. Image Commun. 81 (2019).

[44]

Li C., Anwar S., Porikli F., Underwater scene prior inspired deep underwater image and video enhancement, Pattern Recognit. 98 (2020) 107038–107049.

Digital Library

[45]

Land E.H., McCann J.J., Lightness and retinex theory, J. Opt. Soc. Am. 22 (1) (1971) 1–11.

[46]

Jobson D.J., Rahman Z.U., Woodell G.A., Properties and performance of a center/surround retinex, IEEE Trans. Image Process 6 (3) (1997) 451–462.

Digital Library

[47]

Jobson D.J., Rahman Z.U., Woodell G.A., A multiscale retinex for bridging the gap between color images and the human observation of scenes, IEEE Trans. Image Process 6 (7) (2002) 965–976.

Digital Library

[48]

Rahman Z.U., Jobson D.J., Woodell G.A., Retinex processing for automatic image enhancement, J. Electron. Imag. 13 (2004) 100–110.

[49]

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

[50]

Zhang S., Wang T., Dong J., Yu H., Underwater image enhancement via extended multi-scale retinex, Neurocomputing 245 (2017) 1–9.

Digital Library

[51]

Panetta K.A., Wharton E.J., Agaian S.S., Human visual system-based image enhancement and logarithmic contrast measure, IEEE Trans. Syst. Man Cybern. 38 (1) (2008) 174–188.

[52]

Zhang X., Gao S., Li C., Li Y., A retina inspired model for enhancing visibility of hazy images, Front. Comput. Neurosci. 9 (2015).

[53]

Gao S., Zhang M., Zhao Q., Zhang X., Li Y., Underwater image enhancement using adaptive retinal mechanisms, IEEE Trans. Image Process 28 (5580–5595) (2019).

[54]

Yang K., Zhang X., Li Y., A biological vision inspired framework for image enhancement in poor visibility conditions, IEEE Trans. Image Process 29 (2020) 1493–1506.

[55]

Sánchez-Ferreira C., Coelho L.S., Ayala H., Farias M.C.Q., Llanos C.H., Bio-inspired optimization algorithms for real underwater image restoration, Signal Process. Image Commun. 77 (2019) 49–65.

Digital Library

[56]

L. Zhu, S. Dong, J. Li, T. Huang, Y. Tian, Retina-like visual image reconstruction via spiking neural model, in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2020, pp. 1435–1443.

[57]

Gao S., Tan M., He Z., Tone mapping beyond the classical receptive field, IEEE Trans. Image Process 29 (2020) 4174–4187.

[58]

Land E.H., The retinex theory of color vision, Sci. Am. 237 (6) (1977) 108–128.

[59]

Buchsbaum G., A spatial processor model for object colour perception, J. Franklin Inst. 310 (1) (1980) 1–26.

[60]

Gao S., Ren Y., Zhang M., Li Y., Combining bottom-up and top-down visual mechanisms for color constancy under varying illumination, IEEE Trans. Image Process. 28 (9) (2019) 4387–4400.

[61]

van de Weijer J., Gevers T., Gijsenij A., Edge-based color constancy, IEEE Trans. Image Process 16 (2007) 2207–2214.

[62]

G.D. Finlayson, E. Trezzi, Shades of gray and colour constancy, in: Color and Imaging Conference, 2004, pp. 37–41.

[63]

Dartnall H.J.A., Bowmaker J.K., Mollon J.D., Human visual pigments: Microspectrophotometric results from the eyes of seven persons, Proc. Royal Soc. B: Biol. Ences 220 (1218) (1983) 115–130.

[64]

Masland R.H., The neuronal organization of the retina, Neuron 76 (2) (2012) 266–280.

[65]

Vanleeuwen M., Joselevitch C., Fahrenfort I., Kamermans M., The contribution of the outer retina to color constancy: A general model for color constancy synthesized from primate and fish data, Vis. Neuroence 24 (3) (2007) 277–290.

[66]

Wang Y., Yang J., Yin W., Zhang Y., A new alternating minimization algorithm for total variation image reconstruction, SIAM J. Imag. Ences 1 (3) (2008) 248–272.

[67]

X. Pu, K. Yang, Y. Li, A retinal adaptation model for HDR image compression, in: CCF Chinese Conference on Computer Vision, 2017, pp. 37–47.

[68]

Y. Li, S. Gao, W. Hang, R. Li, C. Li, Local regions with normal brightness contribute more to color constancy, in: I-Perception, 2014.

[69]

Sharma G., Wu W., Dalal E.N., The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations, Color Res. Appl. 30 (1) (2010) 21–30.

[70]

Li C., Guo C., Ren W., Cong R., Hou J., Kwong S., Tao D., An underwater image enhancement benchmark dataset and beyond, IEEE Trans. Image Process 29 (2020) 4376–4389.

[71]

Marques T.P., Albu A.B., Hoeberechts M., A contrast-guided approach for the enhancement of low-lighting underwater images, J. Imaging (2019).

[72]

Crete F., Dolmiere T., Ladret P., Nicolas M., The blur effect: Perception and estimation with a new no-reference perceptual blur metric, in: Human Vision and Electronic Imaging XII, 2007.

[73]

Panetta K., Gao C., Agaian S., Human-visual-system-inspired underwater image quality measures, IEEE J. Ocean. Eng. 41 (3) (2016) 541–551.

[74]

Miao Y., Arcot S., An underwater color image quality evaluation metric, IEEE Trans. Image Process 24 (12) (2015) 6062–6071.

[75]

B. Ortiz-Jaramillo, A. Kumcu, L. Platisa, W. Philips, Computing contrast ratio in images using local content information, in: Signal Process. Images Comput. Vis., 2015, pp. 1–6.

Recommendations

Underwater image enhancement by color correction and color constancy via Retinex for detail preserving
Abstract
In underwater, light attenuation causes non-uniform illumination that degrades underwater image. To enhance the degraded image, we propose an underwater image enhancement method that includes color correction, color constancy, multi-...
Graphical abstract

Display Omitted
Highlights
- The formulation of color correction compensates the red and blue channels by masking.
An Underwater Color Image Enhancement Model Based on Comprehensive Processing
ICNSER2020: Proceedings of the 2nd International Conference on Industrial Control Network And System Engineering Research

The propagation of light in water is affected by the absorption of water and the scattering of particles in water, which leads to the problems of low contrast, blur, color distortion and so on, the traditional underwater image enhancement algorithm is ...
Underwater image enhancement by combining color constancy and dehazing based on depth estimation
Highlights
- Proposed an underwater image enhancement method using color constancy and dehazing.
Abstract
The physical properties which are present in the underwater environment affects the images captured by the visual sensors. As a consequence of these properties, the captured image includes non-uniform illumination. This non-uniform ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Image Communication

Image Communication Volume 104, Issue C

May 2022

131 pages

ISSN:0923-5965

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 May 2022

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

0
Total Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents