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Image Processing and Classification of Underwater Reef Images

Sujata Bhairnallykar, Deeshant Dinesh Singh, Akhil Shailendra Sharma, Nitesh Mayaram Yadav


The oceans play a crucial part in the cycle of life on earth by containing undiscovered items and materials as well as abundant energy supplies. Many scientists/researchers are doing their research on such objects, and for these research, they required a clear quality of underwater images. While these underwater images play an important role in ocean exploration, the absorption and scattering of light in aquatic media often results in significantly reduced quality. The use of new approaches for enhancing the quality of underwater photos is ongoing to create images of higher quality, even though there have lately been significant advancements in the field of image processing. Here, we describe how to enhance and restore images to deal with typical underwater image degradation, such as extreme degradation and distortion. Here we are trying to reduce the noises, to sharpen or brighten images and to restore in clean or original underwater images by using various image processing techniques and, we are using convolutional neural network for classifying some of the major types of coral reef such as Pocillopora, Turf, Sand, Pocill, Macro and Porit i.e., Underwater Image Classification.

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