Deep Learning Based Underwater Image Enhancement Using Deep Convolution Neural Network

Underwater Image Enhancement (UIE) has received a lot of attention due to increased civilian and military uses, though there has been substantial progress in this area. Underwater photography, on the other hand, has low contrast and unclear features due to light absorption and scattering. Deep learning has become extremely prevalent in underwater image enhancement and restoration in recent times because of its extensive feature learning abilities, yet precise enhancement still has problems. To address this issue, we have proposed a UIE approach using Deep Learning (DL) techniques. A Deep Convolution Neural Network (CNN) framework for underwater IE and restoration by channelling the damaged underwater image and extracting multi-contextual information. The experiments were performed on the EUVP (Enhancing Underwater Visual Perception) dataset and the results outline that the recommended approach outperforms the other most recent methodologies and gives efficient outcomes.