Non-local means(NLM)method is a state-of-the-art denoising algorithm, which replaces each pixel with a weighted average of all the pixels in the image. However, the huge computational complexity makes it impractical f...Non-local means(NLM)method is a state-of-the-art denoising algorithm, which replaces each pixel with a weighted average of all the pixels in the image. However, the huge computational complexity makes it impractical for real applications. Thus, a fast non-local means algorithm based on Krawtchouk moments is proposed to improve the denoising performance and reduce the computing time. Krawtchouk moments of each image patch are calculated and used in the subsequent similarity measure in order to perform a weighted averaging. Instead of computing the Euclidean distance of two image patches, the similarity measure is obtained by low-order Krawtchouk moments, which can reduce a lot of computational complexity. Since Krawtchouk moments can extract local features and have a good antinoise ability, they can classify the useful information out of noise and provide an accurate similarity measure. Detailed experiments demonstrate that the proposed method outperforms the original NLM method and other moment-based methods according to a comprehensive consideration on subjective visual quality, method noise, peak signal to noise ratio(PSNR), structural similarity(SSIM) index and computing time. Most importantly, the proposed method is around 35 times faster than the original NLM method.展开更多
变电站的巡检机器人在执行巡检任务时,由于位置的变动导致采集的现场设备图像内容出现差异。针对传统定点拍摄的图像处理方法不能满足变电站设备图像处理要求的问题,该文提出了一种基于标记分水岭模型与Krawtchouk不变矩相结合的改进算...变电站的巡检机器人在执行巡检任务时,由于位置的变动导致采集的现场设备图像内容出现差异。针对传统定点拍摄的图像处理方法不能满足变电站设备图像处理要求的问题,该文提出了一种基于标记分水岭模型与Krawtchouk不变矩相结合的改进算法,以实现巡检图像中的设备存在遮挡物及仿射变换情况下的目标分割与特征量提取,基于Hsim函数完成最终的目标识别。以松原500 k V变电站巡检机器人实际拍摄的图像为例,结果证明所提算法能够对设备图像进行有效的分割与分类,并通过特征量匹配实现对变电站设备现场状态的甄别,为变电站现场设备图像智能处理提供一种可行的方案。展开更多
基金Supported by the Open Fund of State Key Laboratory of Marine Geology,Tongji University(No.MGK1412)Open Fund(No.PLN1303)of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(Southwest Petroleum University)+2 种基金Open Fund of Jiangsu Key Laboratory of Quality Control and Further Processing of Cereals and Oils,Nanjing University of Finance Economics(No.LYPK201304)Foundation of Graduate Innovation Center in NUAA(No.kfjj201430)Fundamental Research Funds for the Central Universities
文摘Non-local means(NLM)method is a state-of-the-art denoising algorithm, which replaces each pixel with a weighted average of all the pixels in the image. However, the huge computational complexity makes it impractical for real applications. Thus, a fast non-local means algorithm based on Krawtchouk moments is proposed to improve the denoising performance and reduce the computing time. Krawtchouk moments of each image patch are calculated and used in the subsequent similarity measure in order to perform a weighted averaging. Instead of computing the Euclidean distance of two image patches, the similarity measure is obtained by low-order Krawtchouk moments, which can reduce a lot of computational complexity. Since Krawtchouk moments can extract local features and have a good antinoise ability, they can classify the useful information out of noise and provide an accurate similarity measure. Detailed experiments demonstrate that the proposed method outperforms the original NLM method and other moment-based methods according to a comprehensive consideration on subjective visual quality, method noise, peak signal to noise ratio(PSNR), structural similarity(SSIM) index and computing time. Most importantly, the proposed method is around 35 times faster than the original NLM method.
文摘变电站的巡检机器人在执行巡检任务时,由于位置的变动导致采集的现场设备图像内容出现差异。针对传统定点拍摄的图像处理方法不能满足变电站设备图像处理要求的问题,该文提出了一种基于标记分水岭模型与Krawtchouk不变矩相结合的改进算法,以实现巡检图像中的设备存在遮挡物及仿射变换情况下的目标分割与特征量提取,基于Hsim函数完成最终的目标识别。以松原500 k V变电站巡检机器人实际拍摄的图像为例,结果证明所提算法能够对设备图像进行有效的分割与分类,并通过特征量匹配实现对变电站设备现场状态的甄别,为变电站现场设备图像智能处理提供一种可行的方案。
