Underwater Image Enhancement and Object Recognition Using CNN Algorithm

The research aims to improve underwater image quality due to the scattering and absorption of light, resulting in poor image quality and reduced visibility. In this study, we propose a novel approach for underwater image enhancement and object recognition using Convolutional Neural Network, or CNN algorithms with the integration of a median filter, implemented in MATLAB. The median filter serves as a preprocessing step to reduce noise and enhance image clarity, while the CNN algorithm is employed for object recognition in the enhanced images. Experimental results demonstrate significant improvements in image quality and object recognition accuracy compared to existing methods. The proposed framework offers a promising solution for underwater applications such as marine biology, underwater surveillance, and underwater robotics, where clear imaging and accurate object detection are essential.