%0 Journal Article %T 基于非稳态随机过程的近红外反射率鲁棒估计算法 %T Nonstationary stochastic process-based robust estimation algorithm of near-infrared albedo %A 房卓群 %A 于晓升 %A 贾同 %A 吴成 %A 李永强 %A 许茗 %A FANG,Zhuo qun %A YU,Xiao sheng %A JIA,Tong %A WU,Cheng dong %A LI,Yong qiang %A XU,Ming %J 控制与决策 %J Control and Decision %@ 1001-0920 %V 34 %N 6 %D 2019 %P 1151-1159 %K 近红外光;反射率;鲁棒估计;随机过程;深度图像;红外图像 %K near infrared;albedo;robust estimation;stochastic process;depth image;infrared image %X 反射率估计在计算机视觉、计算机图形学等领域具有重要作用.为了精确获取反射率,提出一种基于非稳态随机过程的近红外反射率鲁棒估计算法(RENA).该算法以Kinect二代传感器采集结果计算初始反射率,并建立反射率加性噪声模型,同时提出光照度鲁棒估计的概念,简化反射率图像非稳态随机过程模型.实验表明,RENA算法的反射率估计结果优于其他去噪算法,适用于室内场景的反射率图像高精度估计. %X Albedo estimation plays an important role in many areas such as computer vision, computer graphics etc. A robust estimation algorithm of near-infrared albedo (RENA) based on nonstationary stochastic process is proposed in order to obtain albedo with high quality. This algorithm takes Kinect one as input and establishes an additive noise model of albedo. Simultaneously, the concept of robust shading estimation is proposed to simplify the nonstationary stochastic process model of albedo. Experiments show that estimation results of the proposed algorithm are better than other denoising algorithms, and it is suitable for high precision estimation of albedo images in indoor scenes. %R 10.13195/j.kzyjc.2017.1617 %U http://kzyjc.alljournals.cn/kzyjc/home %1 JIS Version 3.0.0