Abstract
Most of the microscopic images of Harmful Algae Blooms (HABs) acquired in marine engineering are subject to blurred cell textures and poor overall clarity due to the effects of seawater impurities, unknown suspended particle deposits and high-speed cell motions. To solve the above problems, a microscopic image enhancement method based on the recursive-overlapped contrast limited adaptive histogram specification (CLAHS) and dual-image wavelet fusion (RO-CLAHS and DIWF) is proposed in this paper. It combines three main steps: homomorphic filtering, empirical modal feature map extraction, and dual-image wavelet fusion. This method firstly adopts homomorphic filtering to strengthen the illumination uniformity of the entire HABs image, and then obtains an empirical modal feature map of algal cells by an improved empirical modal decomposition method, which highlights the detailed features of algal cells due to histogram stretching, clip limit and grey-level map**. Finally, this paper proposes a dual-image wavelet fusion method adapted for HAB microscopic images, which integrates the images processed by contrast limited adaptive histogram equalization (CLAHE) with an empirical modal feature map and then achieves the image enhancement effect. The comparison experimental results show that the enhanced microscopic images of HABs by this method presents the best texture clarity and global contrast and improves the accuracy of algae image edge detection significantly.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10489-023-04502-x/MediaObjects/10489_2023_4502_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10489-023-04502-x/MediaObjects/10489_2023_4502_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10489-023-04502-x/MediaObjects/10489_2023_4502_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10489-023-04502-x/MediaObjects/10489_2023_4502_Figd_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10489-023-04502-x/MediaObjects/10489_2023_4502_Fige_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10489-023-04502-x/MediaObjects/10489_2023_4502_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10489-023-04502-x/MediaObjects/10489_2023_4502_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10489-023-04502-x/MediaObjects/10489_2023_4502_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10489-023-04502-x/MediaObjects/10489_2023_4502_Fig7_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10489-023-04502-x/MediaObjects/10489_2023_4502_Fig8_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10489-023-04502-x/MediaObjects/10489_2023_4502_Fig9_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10489-023-04502-x/MediaObjects/10489_2023_4502_Fig10_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10489-023-04502-x/MediaObjects/10489_2023_4502_Fig11_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10489-023-04502-x/MediaObjects/10489_2023_4502_Fig12_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10489-023-04502-x/MediaObjects/10489_2023_4502_Fig13_HTML.png)
Similar content being viewed by others
References
Agaian SS, Panetta K, Grigoryan AM (2000, September) A new measure of image enhancement. In: IASTED international conference on Signal Processing & Communication. Citeseer, pp. 19-22
Agaian SS, Silver B, Panetta KA (2007) Transform coefficient histogram-based image enhancement algorithms using contrast entropy. IEEE Trans Image Process 16(3):741–758
Aktar MS, Khalilullah KI, Abrahim S, Hamid ME (2016) BEMD-HT based RGB color image robust information hiding algorithm using block averaging technique. Int J Signal Process Image Process Pattern Recognit 9(12):153–166
Ancuti CO, Ancuti C, De Vleeschouwer C, Bekaert P (2017) Color balance and fusion for underwater image enhancement. IEEE Trans Image Process 27(1):379–393
Arbelaez P, Maire M, Fowlkes C, Malik J (2010) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916
Azmi KZM, Ghani ASA, Yusof ZM, Ibrahim Z (2019) Natural-based underwater image color enhancement through fusion of swarm-intelligence algorithm. Appl Soft Comput 85:105810
Draredja MA, Frihi H, Boualleg C, Gofart A, Abadie E, Laabir M (2019) Seasonal variations of phytoplankton community in relation to environmental factors in a protected meso-oligotrophic southern Mediterranean marine ecosystem (Mellah lagoon, Algeria) with an emphasis of HAB species. Environ Monit Assess 191(10):1–17
Ghani ASA (2018) Image contrast enhancement using an integration of recursive-overlapped contrast limited adaptive histogram specification and dual-image wavelet fusion for the high visibility of deep underwater image. Ocean Eng 162:224–238
Glibert PM, Maranger R, Sobota DJ, Bouwman L (2020) Further evidence of the Haber-Bosch—harmful algal bloom (HB-HAB) link and the risk of suggesting HAB control through phosphorus reductions only. In just enough nitrogen. Springer, Cham, pp. 255–282
He X, Wu P, Wang S, Wang A, Wang C, Ding P (2021) Inactivation of harmful algae using photocatalysts: mechanisms and performance. J Clean Prod 289:125755
Hu X, Zhang W, Hu Z (2018) Underwater color image restoration algorithm based on improved prior dark-channel model. J Yangzhou Univ: Natural Sci Ed 21(4):37–41
Jiang Q, Zhang Y, Bao F, Zhao X, Zhang C, Liu P (2022) Two-step domain adaptation for underwater image enhancement. Pattern Recogn 122:108324
Koh JEW, Jahmunah V, Pham TH, Oh SL, Ciaccio EJ, Acharya UR, Yeong CH, Fabell MKM, Rahmat K, Vijayananthan A, Ramli N (2020) Automated detection of Alzheimer's disease using bi-directional empirical model decomposition. Pattern Recogn Lett 135:106–113
Kovalev VA, Liauchuk VA, Voynov DM, Tuzikov AV (2021) Biomedical image recognition in pulmonology and oncology with the use of deep learning. Pattern Recognit Image Anal 31(1):144–162
Li C, Quo J, Pang Y, Chen S, Wang J (2016, March) Single underwater image restoration by blue-green channels dehazing and red channel correction. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp. 1731-1735
Li X, Yang Z, Shang M, Hao J (2016, April) Underwater image enhancement via dark channel prior and luminance adjustment. In: OCEANS 2016-Shanghai. IEEE., pp. 1-5
Li C, Anwar S, Porikli F (2020) Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recogn 98:107038
Li C, Zhang J, Mamunur Rahaman M, Yao Y, Ma P, Zhang J, ..., Grzegorzek M (2021) A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches. ar**v e-prints, ar**v-2103
Liu X, Gao Z, Chen BM (2019) MLFcGAN: multilevel feature fusion-based conditional GAN for underwater image color correction. IEEE Geosci Remote Sens Lett 17(9):1488–1492
Lu Y, Lu R (2018) Fast Bi-dimensional empirical mode decomposition as an image enhancement technique for fruit defect detection. Comput Electron Agric 152:314–323
Ma X, Zhou X, An F (2019) Fast bi-dimensional empirical mode decomposition (BEMD) based on variable neighborhood window method. Multimed Tools Appl 78(7):8889–8910
Min H, **a L, Han J, Wang X, Pan Q, Fu H, Wang H, Wong STC, Li H (2019) A multi-scale level set method based on local features for segmentation of images with intensity inhomogeneity. Pattern Recogn 91:69–85
Pan PW, Yuan F, Cheng E (2018) Underwater image de-scattering and enhancing using dehazenet and HWD. J Mar Sci Technol 26(4):6
Peng YT, Cosman PC (2017) Underwater image restoration based on image blurriness and light absorption. IEEE Trans Image Process 26(4):1579–1594
Poma XS, Riba E, Sappa A (2020) Dense extreme inception network: towards a robust cnn model for edge detection. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision. pp. 1923-1932
Sethi R, Indu S (2020) Fusion of underwater image enhancement and restoration. Int J Pattern Recognit Artif Intell 34(03):2054007
Shan S, Zhang W, Wang X, Tong M (2020, October) Automated red tide algae recognition by the color microscopic image. In 2020 13th international congress on image and signal processing, BioMedical engineering and informatics (CISP-BMEI). IEEE, pp. 852-861
Song W, Wang Y, Huang D, Tjondronegoro D (2018, September) A rapid scene depth estimation model based on underwater light attenuation prior for underwater image restoration. In: Pacific rim conference on Multimedia. Springer, Cham, pp. 678-688
Thilagaraj M, Rajasekaran MP (2019) An empirical mode decomposition (EMD)-based scheme for alcoholism identification. Pattern Recogn Lett 125:133–139
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612
Wang H, Wang Y, Zhang Z, Fu X, Zhuo L, Xu M, Wang M (2020) Kernelized multiview subspace analysis by self-weighted learning. IEEE Trans Multimedia 23:3828–3840
Wang H, Peng J, Zhao Y, Fu X (2020) Multi-path deep cnns for fine-grained car recognition. IEEE Trans Veh Technol 69(10):10484–10493
Wang H, Peng J, Chen D, Jiang G, Zhao T, Fu X (2020) Attribute-guided feature learning network for vehicle reidentification. IEEE MultiMedia 27(4):112–121
Wang H, Peng J, Jiang G, Xu F, Fu X (2021) Discriminative feature and dictionary learning with part-aware model for vehicle re-identification. Neurocomputing 438:55–62
Wang H, Jiang G, Peng J, Deng R, Fu X (2022) Towards adaptive consensus graph: multi-view clustering via graph collaboration. IEEE Trans Multimedia:1–13
Wu J, Huang H, Qiu Y, Wu H, Tian J, Liu J (2005, July) Remote sensing image fusion based on average gradient of wavelet transform. In: IEEE international conference mechatronics and automation, IEEE, 2005 (Vol. 4, pp. 1817-1821)
Yoo J, Uh Y, Chun S, Kang B, Ha JW (2019) Photorealistic style transfer via wavelet transforms. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 9036-9045
Yuille AL, Liu C (2021) Deep nets: what have they ever done for vision? Int J Comput Vis 129(3):781–802
Zhang S, Wang T, Dong J, Yu H (2017) Underwater image enhancement via extended multi-scale Retinex. Neurocomputing 245:1–9
Zuiderveld K (1994) Contrast limited adaptive histogram equalization. Graphics gems, 474-485
Acknowledgments
The authors are grateful for the collaborative funding support from the Shandong Natural Science Foundation of China (ZR2021MD063).
Data availability statements
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Funding
This work was supported by Shandong Natural Science Foundation of China (ZR2021MD063).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wu, GK., Zhang, BP. & Xu, J. Numerical computation of ocean HABs image enhancement based on empirical mode decomposition and wavelet fusion. Appl Intell 53, 19338–19355 (2023). https://doi.org/10.1007/s10489-023-04502-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-023-04502-x