CNN Based Model for Underwater Image Classification and Enhancement

The challenge of enhancing underwater images is difficult because of several issues, including color distortion, inadequate illumination, and dispersion from suspended particles in the water. Conventional picture enhancing methods frequently find it difficult to resolve these problems. Thanks to its ability to learn intricate representations and pick up on minute features, deep learning has become a potent tool for picture improvement tasks in recent years. In this work, we suggest a novel deep learning-based method for improving underwater images. To automatically learn and improve the visual quality of underwater scenes, we use a convolutional neural network architecture that is specifically made to handle underwater photos. A sizable dataset of underwater photos is used to train the network, with an emphasis on contrast improvement and color correction. We compare our method with many state-of-the-art techniques and undertake extensive tests on a benchmark dataset to evaluate our approach. The outcomes show that our strategy performs much better than current approaches both in terms of subjective visual quality and objective criteria. Our technique improves visibility, minimizes noise, and successfully restores colors in underwater photos. By presenting a deep learning-based method that enhances the visual quality of underwater photographs, this work advances the field of underwater image processing and aids in marine research, underwater exploration, and surveillance.