Enhanced underwater fish image processing by noise elimination, saturation adjustment, and edge sharpening technique
Khan, Samra Urooj (University Malaysia Pahang Al-Sultan Abdullah (Malàisia))
Faisal, Sundas (Silesian University of Technology)
Ghazali, Kamarul Hawari (University Malaysia Pahang Al-Sultan Abdullah (Malàisia))

Fecha:	2026
Resumen:	Analyzing underwater images and improving their quality is a difficult task for researchers. Processing underwater images is extremely difficult because of the low contrast, noise, and blurriness brought on by light scattering and absorption. Since the longer wavelengths of sunshine are unable to get deep into the water due to salinity and an abundance of dissolved pollutants, underwater image becomes a crucial research topic, but the photos appear blurry, noisy, and faded. By combining bilateral filtering for decreased noise, saturation improvement for color restoration, and Laplacian sharpening for clarity, this study suggests a novel method for improving underwater image quality. The technique maintains structural integrity while improving visibility. Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM) are used to assess the suggested approach, and the results show notable gains in image quality and clarity. According to the findings, this method improves underwater images, which makes them better suited for tracking the environment, marine object detection, and aquatic research.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Publicado en:	ELCVIA, Vol. 25, Num. 1 (2026) , p. 42-62 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/2285
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/980000007324
DOI: 10.5565/rev/elcvia.2285

21 p, 6.0 MB

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