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This paper describes the use of a single hydrophone to estimate the motion parameters of an autonomous underwater vehicle (AUV) from the underwater acoustic signal excited by its propulsion motor. First, the frequency range of the hydroacoustic signal radiated by the AUV motor is determined, and a detection and recognition model is designed. In the case of uniform linear motion of the AUV, the geometric relationship between the Doppler frequency shift curve of the sound source is derived and the motion model of the sound source and sound line propagation is established. An estimation algorithm for the motion parameters of multiple AUVs based on data from a single hydrophone is derived. Then, for Doppler underwater acoustic signals disturbed by independent identically distributed noise with an arbitrary probability distribution, a cumulative phase difference power amplification instantaneous frequency estimation method is proposed. This method is based on the sum of multiple logarithmic functions. Finally, the effectiveness and accuracy of the algorithm in estimating the motion parameters of multiple AUVs are verified through simulations and experiments.

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Recent advances in deep learning have shown exciting promise in various artificial intelligence vision tasks, such as image classification, image noise reduction, object detection, semantic segmentation, and more. The restoration of the image captured in an extremely dark environment is one of the subtasks in computer vision. Some of the latest progress in this field depends on sophisticated algorithms and massive image pairs taken in low-light and normal-light conditions. However, it is difficult to capture pictures of the same size and the same location under two different light level environments. We propose a method named NL2LL to collect the underexposure images and the corresponding normal exposure images by adjusting camera settings in the “normal” level of light during the daytime. The normal light of the daytime provides better conditions for taking high-quality image pairs quickly and accurately. Additionally, we describe the regularized denoising autoencoder is effective for restoring a low-light image. Due to high-quality training data, the proposed restoration algorithm achieves superior results for images taken in an extremely low-light environment (about 100× underexposure). Our algorithm surpasses most contrasted methods solely relying on a small amount of training data, 20 image pairs. The experiment also shows the model adapts to different brightness environments.

Underwater image processing is a difficult subtopic in the field of computer vision due to the complex underwater environment. Since the light is absorbed and scattered, underwater images have many distortions such as underexposure, blurriness, and color cast. The poor quality hinders subsequent processing such as image classification, object detection, or segmentation. In this paper, we propose a method to collect underwater image pairs by placing two tanks in front of the camera. Due to the high-quality training data, the proposed restoration algorithm based on deep learning achieves inspiring results for underwater images taken in a low-light environment. The proposed method solves two of the most challenging problems for underwater image: darkness and fuzziness. The experimental results show that the proposed method surpasses most other methods.