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@article{179247,
        author = {Nagaraja S R and Pathan Baba Fakruddin and N Bansi Bhargav and VeeramReddy Nithin and N Darshan and RAMYASHREE H G},
        title = {Under Water Image Enhancement},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8806-8811},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179247},
        abstract = {Underwater image quality improvement is imperative for marine science, underwater robots, and ocean exploration, particularly in conditions where visibility is degraded because of turbidity, color aberration, and low contrast. The suggested project develops a predictive model for enhancing underwater image quality based on machine learning algorithms to enhance visual quality. The system uses environmental metadata (e.g., depth, lighting levels, and turbidity) in addition to pixel-level image data to restore clarity, contrast, and natural color balance. Feature correction and noise reduction is investigated using algorithms such as Convolutional Neural Networks (CNNs), Random Forests, and Support Vector Machines (SVMs). A warning mechanism involving buzzers or signal LEDs may be incorporated into autonomous systems to inform when visual quality falls below navigation or analysis critical thresholds.},
        keywords = {Underwater Image Enhancement, Machine Learning, Python, CNN, Image Processing},
        month = {May},
        }