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@article{195586,
        author = {Prasad M. Bhakare and Ishan S. Kulkarni and Aditya P. Bhole and Kapirath D. Raina and Prof. S. N. Firme},
        title = {Ocean-water Imagery Enhancement using CNN and GAN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {2408-2413},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195586},
        abstract = {Poor underwater imagery, due to limited illumination, scattering of light by water particles, and distorted colour representation, often detracts from its usefulness in practical applications for marine research, defence operations, and environmental monitoring. To overcome such challenges, we designed a novel approach, which integrates CNNs with GANs for improving the quality of an image. Our model was trained on the EUVP dataset and proved very effective in restoring clarity, recovering real colours, and eliminating noisy artifacts. Experimental results demonstrate significant improvements that result in visually better-quality images with great similarity to real-world scenarios. It opens up the possibility to perform oceanographic analysis more accurately and to explore and exploit marine resources in an overall better and more sustainable way.},
        keywords = {CNN, Computer Seeing, Deep Learning, EUVP Photo Set, GAN, Mixed Model, Photo Change, Seeing Better, Smart Machine, Smart Computer Model, Underwater Photo Fixing.},
        month = {April},
        }