混合相似性权重的非局部均值去噪算法

针对传统非局部均值(NLM)滤波在噪声标准差较大时,加权欧氏距离不能真实反映邻域块相似度的问题,提出一种新的混合相似性权重的非局部均值去噪算法。首先,利用平稳小波变换的特点对噪声图像进行分解,并利用滤波函数对细节子带进行预去噪处理;然后,根据预去噪图像计算块间相似性参考因子,并使用其替换传统NLM算法中高斯核函数;最后,为使相似性权重更符合人眼视觉系统(HVS)特点,使用基于图像结构感知的块奇异值分解(SVD)方法定义邻域间相似性度量,与传统NLM算法相比能更为真实地反映邻域间相似度。实验结果表明,混合相似性权重的非局部均值去噪算法较传统NLM算法在视觉上能更好地保留纹理细节及边缘信息,而且结构相似度(SSIM)指标较传统NLM算法也有一定提高,在噪声标准差较大情况下具有有效性和鲁棒性。

Non-local means denoising algorithm with hybrid similarity weight

In traditional Non-Local Means (NLM) algorithm, the weighted Euclidean norm can not truly reflect the similarity between two neighborhoods under large noise standard deviation. To address this problem, a new NLM denoising algorithm combined with similarity weight was proposed. Firstly, the noise image was decomposed by using the advantages of stationary wavelet transform, and the filtering function was used to predenoise each detailed subband data. Secondly, according to the refined image, the similarity reference factor between the patches was calculated, and it was used to replace Gauss kernel function of the traditional NLM algorithm. Finally, to make the similarity weights more in line with the characteristics of Human Visual System (HVS), the block Singular Value Decomposition (SVD) method based on image structure perception was used to define neighborhood similarity measure, which can more accurately reflect the similarity between neighborhoods compared with the traditional NLM. The experimental results demonstrate that the hybrid similarity weighted NLM algorithm performs better than the traditional NLM in retaining the texture details and edge information, and the Structural SIMilarity (SSIM) index measurement values is also improved in comparison with the traditional NLM algorithm. When the noise standard deviation is large enough, the proposed approach is of effectiveness and robustness.