Improving The Performance of Object Detection in ‎Underwater with Advanced Deep Learning Based Noise ‎Reduction Techniques

  • Authors

    • S. Deepa Assistant Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, ‎Chennai, India
    • A. Umamageswari Associate Professor Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, ‎Chennai, India
    • Sankari A Associate Professor, Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, ‎Ramapuram, Chennai, India
    • L. Sherin Beevi Associate Professor, Department of Computer Science and Engineering, RMD Engineering College, Kavaraipettai, Tamilnadu, ‎India
    • K. Raja Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, ‎India
    https://doi.org/10.14419/3npw0a46

    Received date: July 22, 2025

    Accepted date: August 26, 2025

    Published date: September 4, 2025

  • Red Compensation; Dn-CNN, CLAHE; Gamma Correction; Image Sharpening; White Balancing; SIFT

  • Abstract

    In order to identify objects that could appear suspicious, this investigation suggests an extensive method that includes pre-processing, ‎enhancement, and noise elimination from submarine images. The method integrates the strengths of SIFT (Scale-Invariant Feature ‎Transform) for feature extraction, CLAHE (Contrast Limited Adaptive Histogram Equalization) for enhancing underwater images to ‎accurately identify features, and DnCNN (Deep Convolutional Neural Network) for effective noise removal through dedicated training. The ‎method incorporates edge enhancement, color correction, brightness modulation, and preparatory processing and de-noising. The purpose of ‎these enhancements is to visually highlight suspicious objects in the underwater images. To calculate the effectiveness of the proposed ‎method, testing has been conducted on a variety of underwater image datasets with various settings and suspicious objects. Analysis was ‎done in contrast to current methods for pre-processing, noise reduction, and object recognition. The results were evaluated using measurable ‎performance metrics as SSIM, MSE, and PSNR. The experiment's findings demonstrate that the combined approach outperforms the ‎individual methods, resulting in a higher rate of detection the 71.2% in Dn-CNN and 61.1 in CLAHE, by making suspicious items appear ‎larger and improving the overall clarity and quality of underwater images. Numerous applications in military operations, offshore ‎technology, maritime research and rescue, aquaculture, and other domains make it an essential sensor technology.

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  • How to Cite

    Deepa, S. ., Umamageswari, A., A, S. ., Beevi , L. S. ., & Raja , K. . (2025). Improving The Performance of Object Detection in ‎Underwater with Advanced Deep Learning Based Noise ‎Reduction Techniques. International Journal of Basic and Applied Sciences, 14(5), 131-143. https://doi.org/10.14419/3npw0a46