Image denoising technology is one of the forelands in the field of computer graphic and computer vision. Non-local means method is one of the great performing methods which arouse tremendous research. In this paper, a...Image denoising technology is one of the forelands in the field of computer graphic and computer vision. Non-local means method is one of the great performing methods which arouse tremendous research. In this paper, an improved weighted non-local means algorithm for image denoising is proposed. The non-local means denoising method replaces each pixel by the weighted average of pixels with the surrounding neighborhoods. The proposed method evaluates on testing images with various levels noise. Experimental results show that the algorithm improves the denoising performance.展开更多
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
The non-local means (NLM) denoising method replaces each pixel by the weighted average of pixels with the sur-rounding neighborhoods. In this paper we employ a cosine weighting function instead of the original exponen...The non-local means (NLM) denoising method replaces each pixel by the weighted average of pixels with the sur-rounding neighborhoods. In this paper we employ a cosine weighting function instead of the original exponential func-tion to improve the efficiency of the NLM denoising method. The cosine function outperforms in the high level noise more than low level noise. To increase the performance more in the low level noise we calculate the neighborhood si-milarity weights in a lower-dimensional subspace using singular value decomposition (SVD). Experimental compari-sons between the proposed modifications against the original NLM algorithm demonstrate its superior denoising per-formance in terms of peak signal to noise ratio (PSNR) and histogram, using various test images corrupted by additive white Gaussian noise (AWGN).展开更多
In medical images, exist often a lot of noise, the noise will seriously affect the accuracy of the segmentation results. The traditional standard fuzzy c-means(FCM) algorithm in image segmentation do not taken into ac...In medical images, exist often a lot of noise, the noise will seriously affect the accuracy of the segmentation results. The traditional standard fuzzy c-means(FCM) algorithm in image segmentation do not taken into account the relationship the adjacent pixels, which leads to the standard fuzzy c-means(FCM) algorithm is very sensitive to noise in the image. Proposed improvedfuzzy c-means(FCM) algorithm, taking both the local and non-local information into the standard fuzzy c-means(FCM) clustering algorithm. The ex-periment results can show that the improved algorithm can achieve better effect than other methods of brain tissue segmentation.展开更多
针对非局部均值去噪算法(NLM)易造成图像边缘模糊问题,提出了一种基于双边滤波和离散余弦变换的改进算法。该算法将双边滤波中的像素空间邻近函数与NLM算法的权值函数相结合,提出新的权值计算公式进而保护图像细节;利用离散余弦变换能...针对非局部均值去噪算法(NLM)易造成图像边缘模糊问题,提出了一种基于双边滤波和离散余弦变换的改进算法。该算法将双边滤波中的像素空间邻近函数与NLM算法的权值函数相结合,提出新的权值计算公式进而保护图像细节;利用离散余弦变换能量集中特性来计算像素相似性权值进而提高运算速度。首先将图像分割成子块,对子块进行离散余弦变换,然后在得到的离散余弦变换系数矩阵中筛选数据,最后用新权值计算公式在经筛选的离散余弦变换系数矩阵中度量像素的相似性。实验结果表明,与原NLM相比,该算法更好地保护了图像边缘细节特征和结构信息,峰值信噪比最大提高了1.4 d B,证明本文的算法去噪效果更佳。展开更多
文摘Image denoising technology is one of the forelands in the field of computer graphic and computer vision. Non-local means method is one of the great performing methods which arouse tremendous research. In this paper, an improved weighted non-local means algorithm for image denoising is proposed. The non-local means denoising method replaces each pixel by the weighted average of pixels with the surrounding neighborhoods. The proposed method evaluates on testing images with various levels noise. Experimental results show that the algorithm improves the denoising performance.
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The non-local means (NLM) denoising method replaces each pixel by the weighted average of pixels with the sur-rounding neighborhoods. In this paper we employ a cosine weighting function instead of the original exponential func-tion to improve the efficiency of the NLM denoising method. The cosine function outperforms in the high level noise more than low level noise. To increase the performance more in the low level noise we calculate the neighborhood si-milarity weights in a lower-dimensional subspace using singular value decomposition (SVD). Experimental compari-sons between the proposed modifications against the original NLM algorithm demonstrate its superior denoising per-formance in terms of peak signal to noise ratio (PSNR) and histogram, using various test images corrupted by additive white Gaussian noise (AWGN).
文摘In medical images, exist often a lot of noise, the noise will seriously affect the accuracy of the segmentation results. The traditional standard fuzzy c-means(FCM) algorithm in image segmentation do not taken into account the relationship the adjacent pixels, which leads to the standard fuzzy c-means(FCM) algorithm is very sensitive to noise in the image. Proposed improvedfuzzy c-means(FCM) algorithm, taking both the local and non-local information into the standard fuzzy c-means(FCM) clustering algorithm. The ex-periment results can show that the improved algorithm can achieve better effect than other methods of brain tissue segmentation.
文摘针对非局部均值去噪算法(NLM)易造成图像边缘模糊问题,提出了一种基于双边滤波和离散余弦变换的改进算法。该算法将双边滤波中的像素空间邻近函数与NLM算法的权值函数相结合,提出新的权值计算公式进而保护图像细节;利用离散余弦变换能量集中特性来计算像素相似性权值进而提高运算速度。首先将图像分割成子块,对子块进行离散余弦变换,然后在得到的离散余弦变换系数矩阵中筛选数据,最后用新权值计算公式在经筛选的离散余弦变换系数矩阵中度量像素的相似性。实验结果表明,与原NLM相比,该算法更好地保护了图像边缘细节特征和结构信息,峰值信噪比最大提高了1.4 d B,证明本文的算法去噪效果更佳。
