A Self-Supervised Denoising Strategy for Underwater Acoustic Camera Imageries
<|reference_start|>A Self-Supervised Denoising Strategy for Underwater Acoustic Camera Imageries: In low-visibility marine environments characterized by turbidity and darkness, acoustic cameras serve as visual sensors capable of generating high-resolution 2D sonar images. However, acoustic camera images are interfered with by complex noise and are difficult to be directly ingested by downstream visual algorithms. This paper introduces a novel strategy for denoising acoustic camera images using deep learning techniques, which comprises two principal components: a self-supervised denoising framework and a fine feature-guided block. Additionally, the study explores the relationship between the level of image denoising and the improvement in feature-matching performance. Experimental results show that the proposed denoising strategy can effectively filter acoustic camera images without prior knowledge of the noise model. The denoising process is nearly end-to-end without complex parameter tuning and post-processing. It successfully removes noise while preserving fine feature details, thereby enhancing the performance of local feature matching.<|reference_end|>
@article{zhou2024a,
    title={A Self-Supervised Denoising Strategy for Underwater Acoustic Camera
  Imageries},
    author={Xiaoteng Zhou, Katsunori Mizuno, Yilong Zhang},
    journal={arXiv preprint arXiv:2406.02914},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02914},
    primaryClass={cs.CV eess.IV}
}