Enhancing Underwater Object Detection Using Advanced Deep Learning De-Noising Techniques

This paper proposes an entire strategy that involves pre-processing, augmentation, and noise reduction of submarine photos, which is followed by the identification of things that might seem suspicious.For feature extraction, the method combines the capabilities of DnCNN (Deep Convolutional Neural Network) to remove the noise by proper training, SURF (Speeded-Up Robust Features) to extract the features, and CLAHE (Contrast Limited Adaptive Histogram Equalisation) algorithm to enhance underwater images to identify the features properly.In addition to preliminary processing and de-noising, edge improvement, colour correction, and brightness modification are included into the approach.These improvements are meant to visually draw focus on dubious objects in pictures taken underwater.Extensive tests have been carried out on an assortment of underwater picture dataset comprising different settings and suspicious items in order to assess the efficacy of the suggested method.Analysis conducted in comparison with present techniques for object identification, pre-processing, and noise reduction.The outcomes were assessed using quantitative indicators of performance such PSNR, MSE and SSIM.The results of the experiment show that the combined strategy works better than the distinct techniques, producing higher detection rates of 62.02 in DnCNN and 52.02 in CLAHE by increasing the apparent size of suspicious objects and improving the general quality of underwater photos.It is a crucial sensor technology with numerous uses in the military, offshore technology, aquaculture, marine research and rescue, and other fields.