Evaluating Deep Learning Approaches for Enhanced Underwater Image Quality

This research investigates the application of deep learning techniques, including Deep Convolutional Neural Networks (DCNNs) and Conditional Generative Adversarial Networks (CGANs), for enhancing the quality of underwater images (UWIE). Underwater imaging presents significant challenges due to factors such as low light conditions, color distortion, and limited visibility. This study aims to improve the quality of underwater images by addressing these challenges through advanced image processing techniques. The performance of different UWIE methods is evaluated using metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Underwater Image Quality Metrics (UIQM). The findings of this research contribute to the advancement of underwater imaging technology, enabling improved image quality for various applications, including marine biology, underwater exploration, and underwater archaeology.