A Novel Image Resolution and Clarity Enhancement Scheme for Underwater Image Processing Scheme

The focus of this research is the development of a system designed to improve the quality and efficiency of underwater optical images, taking into account factors such as forward and reverse diffraction, absorption coefficient of light, and marine snow. The system aims to enhance perception by identifying underwater mineral items, contributing to quicker and more accurate classification and identification of mining objects. A key component of this approach is the integration of mine classification methods with object identification, achieved through the use of a random forest classifier.

The proposed methodology involves the integration of Automatic-Detection-and-Classification (ADC) of Underwater Structures for Mines Hunt Systems with the Random Forest Classifier. The integration of ADC and the Random Forest scheme is collectively referred to as the Underwater Image Processing Scheme (UIPS). This scheme demonstrates its effectiveness in providing high-quality image outcomes. The research concludes that the proposed system not only enhances the quality of underwater images but also offers efficient and real-time analysis of objects, particularly those resembling mines. The findings presented in this paper serve as evidence of the effectiveness of the developed Underwater Image Processing Scheme.