A novel deep learning based underwater image de-noising and detecting suspicious object

Abstract

Exploration of underwater resource play a vital role for nation development. Underwater surveillance systems play a crucial role in security applications, requiring accurate detection of suspicious objects in underwater images. However, the presence of noise, poor visibility, and uneven lighting conditions in underwater environments pose significant challenges for reliable object detection. This work proposes an integrated approach for underwater image de-noising, pre-processing, enhancement, and subsequent suspicious object detection by combining the DnCNN (Deep Convolutional Neural Network), CLAHE (Contrast Limited Adaptive Histogram Equalization), and additional image enhancement techniques. In addition to de-noising and pre-processing, it incorporate various image enhancement techniques to further improve object detection performance. These techniques include color correction, contrast adjustment, and edge enhancement, aiming to enhance the visual characteristics and saliency of suspicious objects in underwater images. To evaluate the effectiveness of proposed approach, this work conducted extensive experiments on an underwater image dataset containing diverse scenes and suspicious objects. The work compares proposed method with existing de-noising, preprocessing, and object detection techniques, analyzing the results using quantitative performance metrics, including precision, recall, and F1 score. The experimental results demonstrate that proposed integrated approach outperforms individual methods and achieves superior detection performance by enhancing the quality of underwater images and improving the visibility of suspicious objects.