Abstract
Abstract: Underwater imaging poses significant challenges due to light absorption, scattering, and color distortion. This paper introduces an innovative underwater image restoration system designed to enhance the visibility and analytical capabilities of underwater imaging. The proposed system employs advanced image processing techniques to address the inherent issues associated with underwater photography. The methodology involves the development of a model that accounts for the optical properties of water, including attenuation and scattering. By leveraging this model, the system corrects color distortions and enhances contrast, leading to improved clarity in underwater images. Additionally, a novel algorithm is employed to reduce the impact of particulate matter, such as suspended sediments, contributing to a clearer representation of the underwater scene. The system incorporates machine learning approaches for adaptive filtering, allowing it to dynamically adjust parameters based on environmental conditions. This adaptability enables the restoration system to perform effectively across a range of underwater scenarios, from clear to turbid waters.
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