改进的小波域自适应阈值煤尘图像去噪  

Improved Adaptive Wavelet Thresholding for Coal Dust Image Denoising

作　　者：张伟[1] 

机构地区：[1]青岛科技大学自动化与电子工程学院,山东青岛266042

出　　处：《计算机仿真》2010年第8期247-249,共3页Computer Simulation

基　　金：山东省自然科学基金资助项目(Z2006G06)

摘　　要：煤尘图像在采集和传输过程中受到了噪声的污染。基于Bayes shrinke的小波域自适应阈值去噪算法取得了较好的图像去噪效果,但其阈值的参数估计是在假定噪声和信号不相关情况下得到的,使得其去噪效果降低。提出了一种改进的小波域自适应阈值去噪算法,从而改进了广义高斯分布模型参数的估计方法,并在增加计算量不大的情况下提高了参数估计精度。用改进的小波域自适应阈值去噪算法对煤尘图像进行去噪处理。仿真结果表明,新算法提高了煤尘图像的去噪效果,并且计算量较小,能够满足对煤尘浓度实时测量的要求。Coal dust image is often corrupted by noises in its acquisition or transmission.Adaptive wavelet thresholding denoising algorithm base on Bayes shrinking obtained good denoising result.But its parameters estimate supposes that noise and signal are independent of each other,so its denoising effect is reduced.Proposes an improved adaptive wavelet thresholding denoising algorithm based on improved generalized Gaussian distribution（GGD） parameters estimation method to obtain more high precision with low adding time.The simulation results from using the new denoising algorithm in coal dust image denoising show that it improves coal dust image denoising effect with low time cost.It can be used to real-time measurement coal dust.

关 键 词：广义高斯分布 小波域自适应阈值 图像去噪 煤尘图像 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]