Due to the absorption and scattering of light in water, underwater images commonly exhibit degradation phenomena such as color cast, low visibility, and blurred details. In response to these issues, we propose a color, detail, contrast, and multi-scale fusion underwater image enhancement algorithm called CDCM. The algorithm first uses a color restoration method based on dark and bright channels to effectively correct color distortion of underwater images and restore their natural color balance. Secondly, utilizing morphological operations to enhance the contour and structural information of objects in the image so as to improve detail representation. In addition, the black eagle optimizer (BEO) is introduced and a new fitness function is designed to adaptively optimize image contrast. In the fusion stage, principal component weights are proposed and combined with other weighting strategies to achieve multi-scale image information fusion, enhancing the contrast while preserving rich textures and details. Experimental results on two real underwater image datasets UIEB and RUIE demonstrate that our method effectively reduces degradation phenomena, with image enhancement by improvements in color fidelity, contrast, and detail clarity compared to the existing methods. In terms of objective indicators, our method is also superior to other relevant methods, such as UCIQE, UIQM, AG, IE, PCQI, etc. Our work contributes to advancing underwater image processing techniques.
Acknowledgement
The work was supported by National Natural Science Foundation of China (No.12401703), Natural Science Foundation of Shanxi Province (No.202403021212256), and the 20th Graduate Science and Technology Project of North University of China(No.20242042).
Declaration of conflicting interests
The authors have no conflict of interests related to this publication.
| [1] |
RAVEENDRAN S,PATIL M D,BIRAJDAR G K. Underwater image enhancement: a comprehensive review, recent trends, challenges and applications. Artificial Intelligence Review, 2021, 54(7): 5413-5467.
|
| [2] |
WANG Y,SONG W,FORTINO G,et al. An experimental-based review of image enhancement and image restoration methods for underwater imaging. IEEE Access, 2019, 7: 140233-140251.
|
| [3] |
FAYAZ S,PARAH S A,QURESHI G J,et al. Underwater image restoration: a state-of-the-art review. IET Image Processing, 2021, 15(2): 269-285.
|
| [4] |
PENG Y T,COSMAN P C. Underwater image restoration based on image blurriness and light absorption. IEEE Transactions on Image Processing, 2017, 26(4): 1579-1594.
|
| [5] |
ZHANG M H,PENG J H. Underwater image restoration based on a new underwater image formation model. IEEE Access, 2018, 6: 58634-58644.
|
| [6] |
LI H Y,WANG H,ZHANG Y,et al. Underwater image captioning: Challenges, models, and datasets. ISPRS Journal of Photogrammetry and Remote Sensing, 2025, 220: 440-453.
|
| [7] |
JAFFE J S. Computer modeling and the design of optimal underwater imaging systems. IEEE Journal of Oceanic Engineering, 1990, 15(2): 101-111.
|
| [8] |
NARASIMHAN S G,NAYAR S K. Contrast restoration of weather degraded images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(6): 713-724.
|
| [9] |
HE K M,SUN J,TANG X O. Single image haze removal using dark channel prior//2009 IEEE Conference on Computer Vision and Pattern Recognition, June 20-25, 2009, Miami, FL, USA. New York: IEEE, 2009: 1956-1963.
|
| [10] |
GALDRAN A,PARDO D,PICÓN A,et al. Automatic Red-Channel underwater image restoration. Journal of Visual Communication and Image Representation, 2015, 26: 132-145.
|
| [11] |
DREWS P L J,NASCIMENTO E R,BOTELHO S S C,et al. Underwater depth estimation and image restoration based on single images. IEEE Computer Graphics and Applications, 2016, 36(2): 24-35.
|
| [12] |
DREWS PJr, NASCIMENTO E DO,MORAES F,et al. Transmission estimation in underwater single images//2013 IEEE International Conference on Computer Vision Workshops, December 2-8, 2013, Sydney, NSW, Australia. New York: IEEE, 2014: 825-830.
|
| [13] |
LIANG Z,DING X Y,WANG Y F,et al. GUDCP: generalization of underwater dark channel prior for underwater image restoration. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(7): 4879-4884.
|
| [14] |
AHMAD AWAN H S,MAHMOOD M T. A nonconvex approach with structural priors for restoring underwater images. Mathematics, 2024, 12(22): 3553.
|
| [15] |
REZA A M. Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology, 2004, 38(1): 35-44.
|
| [16] |
CARDEI V C,FUNT B,BARNARD K. White point estimation for uncalibrated images. Color and Imaging Conference, 1999, 7(1): 97-100.
|
| [17] |
LAND E H,MCCANN J J. Lightness and retinex theory. Journal of the Optical Society of America, 1971, 61(1): 1.
|
| [18] |
ANCUTI C O,ANCUTI C,DE VLEESCHOUWER C,et al. Color balance and fusion for underwater image enhancement. IEEE Transactions on Image Processing, 2018, 27(1): 379-393.
|
| [19] |
LIN S J,LI Z,ZHENG F H,et al. Underwater image enhancement based on adaptive color correction and improved retinex algorithm. IEEE Access, 2023, 11: 27620-27630.
|
| [20] |
WANG Z B,ZHOU D J,LI Z C,et al. Underwater image enhancement via adaptive color correction and stationary wavelet detail enhancement. IEEE Access, 2024, 12: 11066-11082.
|
| [21] |
AN S M,XU L H,DENG Z C,et al. HFM: a hybrid fusion method for underwater image enhancement. Engineering Applications of Artificial Intelligence, 2024, 127: 107219.
|
| [22] |
ZHANG W D,WANG Y D,LI C Y. Underwater image enhancement by attenuated color channel correction and detail preserved contrast enhancement. IEEE Journal of Oceanic Engineering, 2022, 47(3): 718-735.
|
| [23] |
ANWAR S,LI C Y. Diving deeper into underwater image enhancement: a survey. Signal Processing: Image Communication, 2020, 89: 115978.
|
| [24] |
GU J X,WANG Z H,KUEN J,et al. Recent advances in convolutional neural networks. Pattern Recognition, 2018, 77: 354-377.
|
| [25] |
LI C Y,GUO C L,REN W Q,et al. An underwater image enhancement benchmark dataset and beyond. IEEE Transactions on Image Processing, 2020, 29: 4376-4389.
|
| [26] |
WU S C,LUO T,JIANG G Y,et al. A two-stage underwater enhancement network based on structure decomposition and characteristics of underwater imaging. IEEE Journal of Oceanic Engineering, 2021, 46(4): 1213-1227.
|
| [27] |
FU Z Q,WANG W,HUANG Y,et al. Uncertainty inspired underwater image enhancement//Computer Vision – ECCV 2022. Cham: Springer Nature Switzerland, 2022: 465-482.
|
| [28] |
Li J,Skinner K A,Eustice R M,et al. WaterGAN: Unsupervised generative network to enable real-time color correction of monocular underwater images. IEEE Robotics and Automation letters, 2018, 3(1): 387-394.
|
| [29] |
WANG Z F,LI C F,MO Y Z,et al. RCA-CycleGAN: Unsupervised underwater image enhancement using red channel attention optimized CycleGAN. Displays, 2023, 76: 102359.
|
| [30] |
ZHANG H B,SAN H J,CHEN J P,et al. Black eagle optimizer: a metaheuristic optimization method for solving engineering optimization problems. Cluster Computing, 2024, 27(9): 12361-12393.
|
| [31] |
YUAN J Y,CAO W,CAI Z C,et al. An underwater image vision enhancement algorithm based on contour Bougie morphology. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(10): 8117-8128.
|
| [32] |
Gonzalez RC,Woods R. Digital image processing: Pearson education india, 2009.
|
| [33] |
YANG C C. Image enhancement by modified contrast-stretching manipulation. Optics & Laser Technology, 2006, 38(3): 196-201.
|
| [34] |
KARAKAYA D,ULUCAN O,TURKAN M. Pas-mef: multi-exposure image fusion based on principal component analysis, adaptive well-exposedness and saliency map//ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, May 23-27, 2022, Singapore, Singapore. New York: IEEE, 2022: 2345-2349.
|
| [35] |
GUO Y T,HU H P,YANG Z M. Low light image enhancement based on multi-scale fusion. Journal of Test and Measurement Technology, 2025, 39(3): 322-329.
|
| [36] |
LIU R S,FAN X,ZHU M,et al. Real-world underwater enhancement: challenges, benchmarks, and solutions under natural light. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(12): 4861-4875.
|
| [37] |
BUCHSBAUM G. A spatial processor model for object colour perception. Journal of the Franklin Institute, 1980, 310(1): 1-26.
|
| [38] |
XIONG N N,SHEN Y,YANG K Y,et al. Color sensors and their applications based on real-time color image segmentation for cyber physical systems. EURASIP Journal on Image and Video Processing, 2018, 2018(1): 23.
|
| [39] |
JANG W,SON K, KIM J,et al. Auto white balance system using adaptive color samples for mobile devices//APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems, November 30 - December 3, 2008, Macao, China. New York: IEEE, 2009: 1462-1465.
|
| [40] |
ZHANG L J,LIU T N,SHI Q C,et al. Underwater image enhancement based on depth and light attenuation estimation. IET Image Processing, 2025, 19(1): e70037.
|
| [41] |
LV S,ZHANG W X,ZHANG M H,et al. Underwater binocular 3D imaging method based on polarization difference ghost imaging. Optics and Lasers in Engineering, 2025, 186: 108856.
|
| [42] |
XUE Q Q,HU H P,BAI Y P,et al. Underwater image enhancement algorithm based on color correction and contrast enhancement. The Visual Computer, 2024, 40(8): 5475-5502.
|
| [43] |
HSIEH Y Z,CHANG M C. Underwater image enhancement and attenuation restoration based on depth and backscatter estimation. IEEE Transactions on Computational Imaging, 2025, 11: 321-332.
|
| [44] |
WANG S Q,MA K D,YEGANEH H,et al. A patch-structure representation method for quality assessment of contrast changed images. IEEE Signal Processing Letters, 2015, 22(12): 2387-2390.
|
PDF
(14979KB)
