基于PURE-LET的激光光斑图像快速去噪算法

煤矿井下低照度成像环境会导致图像中产生泊松噪声，将引起激光光斑图像的强度和形状分布的不确定性，从而影响激光三角测距的精度。提出了一种基于PURE-LET（Poisson Unbiased Risk Estimator-Linear Expansion of Threshholds）的煤位表面激光光斑图像的快速小波域去噪算法。给出了泊松噪声下小波系数估计MSE（Mean Squared Error）的一个无偏估计子PURE，并将小波系数估计子写作一组基本阈值函数的线性组合以提高算法速度。仿真图像与真实煤仓图像的实验显示，与3种典型图像去噪算法（BayesShrink，Poisson_NLMeans，PURE-LET）相比，提出的PURE-LET-Smooth算法具有更好的噪声抑制能力，同时具有保持图像边缘结构和快速计算的特点，这在实时光斑质心精确计算和三角测距应用中是一个明显优势。此外此算法具有阈值函数组合系数的快速自动计算特点，适用于自动煤位检测应用。

PURE-LET-based fast denoising algorithm for laser spot imagery

Low illumination imaging environment in coal mine will lead to the Poisson noise contaminating the imagery，which will cause the uncertainty of spot’s intensity and shape，affecting the accuracy of laser triangulation.This paper presented a fast denoising algorithm for imagery of laser spot on the surface of coal level based on PURE-LET (Poisson Unbiased Risk Estimator-Linear Expansion of Thresholds).Under the Poisson noise，we proposed an unbiased estimator PURE of the Wavelet coefficients estimator MSE (Mean Squared Error).In order to improve the algorithm speed，the estimator of wavelet coefficients was expanded as a set linear combination of basic threshold functions.Denoising results of simulated images and real-world laser spot images show that the proposed algorithm has better performance of computation speed and edge-keeping noise suppression，than three other typical image denoising algorithm (BayesShrink，Poisson_NLMeans，PURE-LET).Additionally this algorithm computes the coefficients of threshold functions automatically，suitable for applications of automatic monitoring of coal levels.