基于Log-Gabor特征的非局部均值去噪算法及其加速方案研究

摘要

非局部均值是一种基于像素长程相似性的图像空域去噪算法，它一般采用灰度块特征估计图像像素间的相似度。文中首先使用基于Log-Gabor特征的像素间相似度估计获得较好的去噪效果。然后将Log-Gabor几何特征与灰度特征相融合，所形成的混合相似度具有更佳的图像局部自适应性，去噪性能也得到进一步提升。最后基于Johnson-Lindenstrauss引理研究利用随机降维方法降低相似度计算的复杂度，并对该加速方案的效果，包括降维前后运行时间对比、降维程度以及随机矩阵生成方法对去噪性能的影响，进行详细试验分析，结果证明基于随机降维的加速方案的有效性。%The nonlocal means ( NLM) is a spatial domain image denoising method, and it exploits long range similarities between pixels of natural images. Notably, the similarity between true pixel values in original NLM is estimated based on patch information of noise-corrupted input image. In this paper, the pixel similarities in NLM are estimated based on Log-Gabor features to achieve good denoising results. Moreover, the mixed similarity combining the Log-Gabor features with intensity information is exploited to get better adaptivity to local image characteristics and further improve the denoising quality. In addition, the random projection-based NLM speed-up method is studied based on Johnson-Lindenstrauss lemma. Extensive tests including the running time comparison before and after dimensionality reduction, the impact of types of projection matrices and the extent of dimensionality reduction on final denoising performances are carried out. The experimental results confirm the effectiveness of the proposed acceleration scheme.