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.
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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
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DOI: https://doi.org/10.1007/978-981-16-6616-2_46
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