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A robust model for improving the quality of underwater images using enhancement techniques

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Abstract

Underwater image enhancement is an important research field that is now being addressed across the world. The primary reason for this is that water scatters and absorbs light, resulting in images with extremely low contrast and color cast. Hence, in order to overcome this issue with underwater images, we designed a simple and effective method. This method is split into two parts. The first section focuses on boosting contrast, while the second on improving color. To begin with, under the RGB colour model, contrast enhancement equalizes the G and B channels. Each R, G, and B channel's histogram is then redistributed using effective parameters connected with the intensity distribution in the input image and the wavelength attenuation of various colors underwater. The noise is subsequently reduced using a bilateral filtering technique, which only keeps important facts in an underwater image but also increases local information. In the second section, the color is enhanced by increasing the L component and modifying the 'a' and 'b' components of the CIE lab color space. Experiment findings show that the suggested method outperforms alternative strategies. Our enhanced results stand out for their brilliant color, greater contrast, and enhanced features. When compared to other approaches, the values of entropy, mean square error (MSE), peak signal to noise ratio (PSNR), underwater color image quality evaluation (UCIQE), and underwater image quality measures are 7.88, 920.20, 18.92, 0.596, and 2.734, respectively. This technique improves image quality by increasing entropy, PSNR, and UCIQE values while lowering MSE. It is an entirely algorithm-based technique that is independent by image datasets. The images used to evaluate the results come from a variety of datasets, and their enhanced performance confirms their robustness. Because of its single image-based approach, our method is very compelling in terms of processing speed. Comprehensive findings on a variety of underwater image datasets demonstrate that our approach outperforms the vast majority of them. For these reasons, the Comparative Universal Stretching approach is better than others.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Delhi Technological University, Delhi, 110042, Delhi, India

    Nishant Singh

  2. Department of Computer Science & Engineering, Delhi Technological University, Delhi, 110042, Delhi, India

    Aruna Bhat

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  1. Nishant Singh
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  2. Aruna Bhat
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Correspondence to Aruna Bhat.

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Singh, N., Bhat, A. A robust model for improving the quality of underwater images using enhancement techniques. Multimed Tools Appl 83, 2267–2288 (2024). https://doi.org/10.1007/s11042-023-15617-1

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