基于深度学习的激光雷达图像稀疏降噪方法  

Sparse denoising method for LiDAR images based on deep learning

作　　者：王永红 王小峰[1] 刘瑞卿 WANG Yonghong;WANG Xiaofeng;LIU Ruiqing(Xinzhou Normal University,Xinzhou Shanxi 034000,China)

机构地区：[1]忻州师范学院,山西忻州034000

出　　处：《激光杂志》2025年第3期154-160,共7页Laser Journal

基　　金：山西省科技攻关项目(No.20232078)。

摘　　要：由于外界环境的影响,激光雷达图像容易受到各种噪声的干扰,降低了数据的准确性。为此,提出基于深度学习的激光雷达图像稀疏降噪方法。采用加速后向投影算法,生成初始的激光雷达图像,针对成像过程中产生的图像模糊现象,通过设定自适应越渡点和增强模糊对比度完成激光雷达图像的去模糊处理。结合深度学习技术的优势,建立自适应栈式修正稀疏降噪自编码器,通过多通道SRDA,每个SDA针对不同类型的噪声进行训练,最后线性组合后可以同时处理多种类型的噪声。这种分通道的方式能够更全面地消除各种噪声,提高了激光雷达图像稀疏降噪的效果。实验结果表明:所提方法在有效去除激光雷达图像模糊现象的同时,具有较高效的降噪能力。Due to the influence of external environment,LiDAR images are easily affected by various noises,which reduces the accuracy of data.To this end,a sparse denoising method for LiDAR images based on deep learning is proposed.We use an accelerated backward projection algorithm to generate initial LiDAR images.In response to the image blurring phenomenon generated during the imaging process,we set adaptive transition points and enhance blurry contrast to complete the deblurring processing of the LiDAR images.Combining the advantages of deep learning technology,an adaptive stack style sparse denoising autoencoder is established.Through multi-channel SRDA,each SDA is trained for different types of noise,and finally linearly combined to handle multiple types of noise simultaneously.This multi-channel approach can more comprehensively eliminate various noises and improve the sparse noise reduction effect of LiDAR images.The experimental results show that the proposed method not only effectively removes the blurring phenomenon of LiDAR images,but also has a relatively efficient denoising ability.

关 键 词：模糊阈值 越渡点 深度学习 激光雷达图像 稀疏降噪 SDA 

分 类 号：TN911[电子电信—通信与信息系统]