Mie lidar signal denoising method based on untrained neural network priors with total variation

Lidar technology is widely used in atmospheric detection, but its performance is often degraded by various noise sources such as solar background radiation and dark current noise. This paper proposes a novel lidar signal denoising method, to our knowledge, UNNP-TV, which combines untrained neural network priors (UNNP) with total variation (TV) regularization to directly reconstruct clean signals from noisy lidar measurements. Unlike traditional denoising approaches that rely solely on prior knowledge of the signal or pre-trained models, UNNP-TV exploits the inherent structural priors of neural networks, enabling effective noise suppression without explicit training. The integration of UNNP and TV regularization mitigates overfitting, stabilizes the denoising process, and enhances signal restoration efficiency. Experimental results demonstrate that UNNP-TV significantly improves the signal-to-noise ratio (SNR) while preserving signal integrity, even in highly noisy environments. Furthermore, the UNNP-TV method is applied to real lidar signals for denoising, and the denoising results show that the method can be adapted to noise suppression in a variety of detection environments and improve the accuracy of aerosol extinction coefficient inversion.