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
Y2000-62122-25 0100479语音处理与识别(含4篇文章)=Session B1:speechprceessing and recognition[会,英]//1999 IEEE Pro-ceedings of Southeastcon’99 Technology on the brink of2000.—25～39(NiK)本部分收录四篇论文,内容涉及用...Y2000-62122-25 0100479语音处理与识别(含4篇文章)=Session B1:speechprceessing and recognition[会,英]//1999 IEEE Pro-ceedings of Southeastcon’99 Technology on the brink of2000.—25～39(NiK)本部分收录四篇论文,内容涉及用于听觉和语音受损的便携式语音识别系统,基于 Unix 的语音数据收集平台,语音识别前端的实施与分析,以及连续语音识别的快速算法。展开更多
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.
文摘Y2000-62122-25 0100479语音处理与识别(含4篇文章)=Session B1:speechprceessing and recognition[会,英]//1999 IEEE Pro-ceedings of Southeastcon’99 Technology on the brink of2000.—25～39(NiK)本部分收录四篇论文,内容涉及用于听觉和语音受损的便携式语音识别系统,基于 Unix 的语音数据收集平台,语音识别前端的实施与分析,以及连续语音识别的快速算法。
