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

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.