非局域均值滤波(non-local means filtering, NLMF)采用图像块间灰度差测度像素间相似性,由于灰度差易受噪声影响,这种相似性测度缺乏鲁棒性。图像块的Zernike矩是块内像素灰度的统计量,且具有旋转无关特性,能在抑制噪声的情况下较好地...非局域均值滤波(non-local means filtering, NLMF)采用图像块间灰度差测度像素间相似性,由于灰度差易受噪声影响,这种相似性测度缺乏鲁棒性。图像块的Zernike矩是块内像素灰度的统计量,且具有旋转无关特性,能在抑制噪声的情况下较好地描述图像块特征。由图像块的各阶Zernike矩差代替灰度差可定义Zernike矩相似度;联合各阶Zernike矩相似度经加权平均可估计出所处理像素的灰度。仿真实验及分析表明文中算法相比直接采用灰度差定义相似度的算法,能更好地去除噪声,获得更高的峰值信噪比(PSNR)。展开更多
Classic non-local means (CNLM) algorithm uses the inherent self-similarity in images for noise removal. The denoised pixel value is estimated through the weighted average of all the pixels in its non-local neighborhoo...Classic non-local means (CNLM) algorithm uses the inherent self-similarity in images for noise removal. The denoised pixel value is estimated through the weighted average of all the pixels in its non-local neighborhood. In the CNLM algorithm, the differences between the pixel value and the distance of the pixel to the center are both taken into consideration to calculate the weighting coefficients. However, the Gaussian kernel cannot reflect the information of edge and structure due to its isotropy, and it has poor performance in flat regions. In this paper, an improved non-local means algorithm based on local edge direction is presented for image denoising. In edge and structure regions, the steering kernel regression (SKR) coefficients are used to calculate the weights, and in flat regions the average kernel is used. Experiments show that the proposed algorithm can effectively protect edge and structure while removing noises better when compared with the CNLM algorithm.展开更多
An innovative multi-robot simultaneous localization and mapping(SLAM)is proposed based on a mobile Ad hoc local wireless sensor network(Ad-WSN).Multiple followed-robots equipped with the wireless link RS232/485module ...An innovative multi-robot simultaneous localization and mapping(SLAM)is proposed based on a mobile Ad hoc local wireless sensor network(Ad-WSN).Multiple followed-robots equipped with the wireless link RS232/485module act as mobile nodes,with various on-board sensors,Tp-link wireless local area network cards,and Tp-link wireless routers.The master robot with embedded industrial PC and a complete robot control system autonomously performs the SLAM task by exchanging information with multiple followed-robots by using this self-organizing mobile wireless network.The PC on the remote console can monitor multi-robot SLAM on-site and provide direct motion control of the robots.This mobile Ad-WSN complements an environment devoid of usual GPS signals for the robots performing SLAM task in search and rescue environments.In post-disaster areas,the network is usually absent or variable and the site scene is cluttered with obstacles.To adapt to such harsh situations,the proposed self-organizing mobile Ad-WSN enables robots to complete the SLAM process while improving the performances of object of interest identification and exploration area coverage.The information of localization and mapping can communicate freely among multiple robots and remote PC control center via this mobile Ad-WSN.Therefore,the autonomous master robot runs SLAM algorithms while exchanging information with multiple followed-robots and with the remote PC control center via this local WSN environment.Simulations and experiments validate the improved performances of the exploration area coverage,object marked,and loop closure,which are adapted to search and rescue post-disaster cluttered environments.展开更多
文摘提出一种连续子邻域内的鲁棒双边滤波算法(Robust Bilateral Filtering)。首先,利用自适应区域生长方法在图像局部邻域中分割出种子像素的连续子邻域;然后,在该连续子邻域中采用改进的双边滤波算法对种子像素值进行平滑处理。为了提高算法的鲁棒性能,类似非局域均值滤波算法(Non-Local Means Filtering),以像素空间临近度和像素局部窗口相似度定义该滤波器核函数。算法结合了双边滤波和非局域均值滤波的优点,且在连续子邻域内进行去噪处理相对可获得更为合理的图像效果。仿真实验表明,该算法具有良好的去噪效果,同时较好地保留了图像的细节特征。
文摘非局域均值滤波(non-local means filtering, NLMF)采用图像块间灰度差测度像素间相似性,由于灰度差易受噪声影响,这种相似性测度缺乏鲁棒性。图像块的Zernike矩是块内像素灰度的统计量,且具有旋转无关特性,能在抑制噪声的情况下较好地描述图像块特征。由图像块的各阶Zernike矩差代替灰度差可定义Zernike矩相似度;联合各阶Zernike矩相似度经加权平均可估计出所处理像素的灰度。仿真实验及分析表明文中算法相比直接采用灰度差定义相似度的算法,能更好地去除噪声,获得更高的峰值信噪比(PSNR)。
基金National Key Research and Development Program of China(No.2016YFC0101601)Fund for Shanxi“1331 Project”Key Innovative Research Team+1 种基金Shanxi Province Science Foundation for Youths(No.201601D021080)Universities Science and Technology Innovation Project of Shanxi Province(No.2017107)
文摘Classic non-local means (CNLM) algorithm uses the inherent self-similarity in images for noise removal. The denoised pixel value is estimated through the weighted average of all the pixels in its non-local neighborhood. In the CNLM algorithm, the differences between the pixel value and the distance of the pixel to the center are both taken into consideration to calculate the weighting coefficients. However, the Gaussian kernel cannot reflect the information of edge and structure due to its isotropy, and it has poor performance in flat regions. In this paper, an improved non-local means algorithm based on local edge direction is presented for image denoising. In edge and structure regions, the steering kernel regression (SKR) coefficients are used to calculate the weights, and in flat regions the average kernel is used. Experiments show that the proposed algorithm can effectively protect edge and structure while removing noises better when compared with the CNLM algorithm.
基金Projects(61573213,61473174,61473179)supported by the National Natural Science Foundation of ChinaProjects(ZR2015PF009,ZR2014FM007)supported by the Natural Science Foundation of Shandong Province,China+1 种基金Project(2014GGX103038)supported by the Shandong Province Science and Technology Development Program,ChinaProject(2014ZZCX04302)supported by the Special Technological Program of Transformation of Initiatively Innovative Achievements in Shandong Province,China
文摘An innovative multi-robot simultaneous localization and mapping(SLAM)is proposed based on a mobile Ad hoc local wireless sensor network(Ad-WSN).Multiple followed-robots equipped with the wireless link RS232/485module act as mobile nodes,with various on-board sensors,Tp-link wireless local area network cards,and Tp-link wireless routers.The master robot with embedded industrial PC and a complete robot control system autonomously performs the SLAM task by exchanging information with multiple followed-robots by using this self-organizing mobile wireless network.The PC on the remote console can monitor multi-robot SLAM on-site and provide direct motion control of the robots.This mobile Ad-WSN complements an environment devoid of usual GPS signals for the robots performing SLAM task in search and rescue environments.In post-disaster areas,the network is usually absent or variable and the site scene is cluttered with obstacles.To adapt to such harsh situations,the proposed self-organizing mobile Ad-WSN enables robots to complete the SLAM process while improving the performances of object of interest identification and exploration area coverage.The information of localization and mapping can communicate freely among multiple robots and remote PC control center via this mobile Ad-WSN.Therefore,the autonomous master robot runs SLAM algorithms while exchanging information with multiple followed-robots and with the remote PC control center via this local WSN environment.Simulations and experiments validate the improved performances of the exploration area coverage,object marked,and loop closure,which are adapted to search and rescue post-disaster cluttered environments.
