Deep Learning Based Algorithm Selection Approach for Underwater Image Processing

Underwater images suffer from distortion due to light properties such as refraction, absorption, and scattering. Although advances in camera technology have improved image quality over time, issues like contrast loss and noise persist. For example, at greater depths, red light is absorbed, leading to colour distortion and a hazy effect. These challenges highlight the critical need for advanced image processing techniques to restore underwater image quality. In this field, many algorithms have been developed to improve underwater image quality. In this paper, we propose an empirical methodology to identify the most suitable algorithm for a given distorted input image.