Abstract
Underwater images suffer from haziness due to the presence of particles and changes in water density in the marine environment. Underwater image segmentation is one of the challenging areas due to the blurriness of the images. Image preprocessing is necessary before image segmentation due to unclear image, so the work is divided into three parts: contrast improvement, sharpening, and segmentation of an image. The fuzzy-based contrast improvement technique is proposed with Contrast Limited Adaptive Histogram Equalization to enhance contrast. An unsharp mask is used to sharpen an image. The proposed partition-based thresholding segmentation is used to segment the image. In this method, the partition is made to calculate an appropriate threshold value to segment each partition. Quantitative and qualitative analysis has been shown in results and discussions part using entropy as a measurement parameter. 75.67% accuracy was received by the work. Also, the work is compared with existing work.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Magudeeswaran, V., Singh, J.F.: Contrast limited fuzzy adaptive histogram equalization for enhancement of brain images, Int. J. Imag. Syst. Technol. 27(1), 98–103, 17
De, S., Dey, S., Paul, S.: Underwater image enhancement using neighbourhood based two level contrast stretching and modified artificial bee colony. In: 7th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON 2020) (Accepted)
Magudeeswaran, V., Ravichandran, C. G.: Fuzzy Logic-Based Histogram Equalization for Image Contrast Enhancement”Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2013, Article ID 891864, 10 pages. https://doi.org/10.1155/2013/891864
Al-Ameen, Z., Muttar, A., Al-Badrani, G.: Improving the sharpness of digital image using an amended unsharp mask filter. Int J Image, Graphics Signal Process 11, 1–9 (2019). https://doi.org/10.5815/ijigsp.2019.03.01.
Rajeev, A.A., Hiranwal, S., Sharma, V.K.: Improved segmentation technique for underwater images based on k-means and local adaptive thresholding. In: Mishra D., Nayak M., Joshi A. (eds) Information and Communication Technology for Sustainable Development. Lecture Notes in Networks and Systems, vol 10. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-3920-1_45
Zhu, Y., Hao, B., Jiang, B., Nian, R., He, B., Ren, X., Lendasse, A.: Underwater image segmentation with co-saliency detection and local statistical active contour model. 1–5 (2017). https://doi.org/10.1109/OCEANSE.2017.8084742
Wu, J., HaitaoGuo: Underwater sonar image segmentation based on snake model. Appl. Mech. Mater. 448–453, 3675–3678 (2013) Oct 31. https://doi.org/10.4028/www.scientific.net/AMM.448-453.3675. ISSN: 1662–7482. © 2014 Trans Tech Publications, Switzerland
Boudhane, M., Nsiri, B.: Underwater image processing method for fish localization and detectionin submarine environment. J. Vis. Commun. Image R. 39, 226–238 (2016). https://doi.org/10.1016/j.jvcir.2016.05.017 1047–3203
Liu, F., Fang, M.: Semantic segmentation of underwater images based on improved deeplab. J. Mar. Sci. Eng. 8, 188 (2020). https://doi.org/10.3390/jmse8030188
Rezaei, F., Izadi, H., Memarian, H., Baniassadi, M.: The effectiveness of different thresholding techniques in segmenting micro CT images of porous carbonates to estimate porosity. J. Petrol. Sci. Eng. (2019). https://doi.org/10.1016/j.petrol.2018.12.063
EL Allaoui, A., Mohammed, M.: Evolutionary region growing for image segmentation. Int. J. Appl. Eng. Res. 13(5), 2084–2090 (2018). ISSN 0973–4562
De, S., Bhattacharyya, S., Dutta, P.: Automatic magnetic resonance image segmentation by fuzzy intercluster hostility index based genetic algorithm: an application. Appl. Soft Comput. 47, 669–683 (2016)
César Mello Román, J., Luis VázquezNoguera, J., Legal-Ayala, H., Pinto-Roa, D.P., Gomez-Guerrero, S., García Torres, M.: Entropy and contrast enhancement of infrared thermal images using the multiscale top-hat transform. Entropy, 21(3), 244 (2019). https://doi.org/10.3390/e21030244
http://www.godac.jamstec.go.jp/catalog/dsdebris/e/index.html. Accessed 22 Jan 2021
Zhang, H., Fritts, J.E., Goldman, S.: An entropy-based objective evaluation method for image segmentation. In: Proceedings of SPIE Storage and Retrieval Methods and Applications for Multimedia, pp. 38–49 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sarkar, P., Gurung, S., De, S. (2022). Underwater Image Segmentation Using Fuzzy-Based Contrast Improvement and Partition-Based Thresholding Technique. In: Bhateja, V., Tang, J., Satapathy, S.C., Peer, P., Das, R. (eds) Evolution in Computational Intelligence. Smart Innovation, Systems and Technologies, vol 267. Springer, Singapore. https://doi.org/10.1007/978-981-16-6616-2_46
Download citation
DOI: https://doi.org/10.1007/978-981-16-6616-2_46
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6615-5
Online ISBN: 978-981-16-6616-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)