基于非局部自相似性的低秩稀疏图像去噪

Nonlocal self-similarity based low-rank spase image denoising

针对许多图像去噪方法在去除噪声的同时容易丢失细节信息的问题,提出了一种基于非局部自相似性的低秩稀疏图像去噪算法。首先,利用基于马氏距离(MD)的块匹配方法将外部自然干净图像块分组,建立基于块组的高斯混合模型(GMM)学习非局部自相似性先验;其次,采用稳健主成分追踪(SPCP)方法,将噪声图像矩阵分解为低秩、稀疏及噪声三部分,其中稀疏矩阵包含了稀疏的有用信息;最后,通过最小化全局目标函数实现去噪。实验结果表明,提出的方法在峰值信噪比(PSNR)及结构相似性(SSIM)的结果上比EPLL、NCSR、PCLR等先进去噪算法都有较大的提升,且速度更快,去噪效果及细节保留能力都有更好的表现。

Focusing on the issue that many image denoising methods are easy to lose detailed information when removing noise,a nonlocal self-similarity based low-rank sparse image denoising method was proposed.Firstly,external natural clean image patches were put into groups by a method of block matching based on Mahalanobis Distance(MD),and then a patch group based Gaussian Mixture Model(GMM)was developed to learn the nonlocal self-similarity prior.Secondly,based on the Stable Principal Component Pursuit(SPCP)method,the noise image matrix was decomposed into low-rank,sparse and noise parts,while the sparse matrix contained useful information.Finally,the global objective function was minimized to achieve denoising.The experimental results show that compared to the previous denoising methods,such as EPLL(Expected Patch Log Likelihood),NCSR(Non-locally Centralized Sparse Representation),PCLR(external Patch prior guided internal CLusteRing),etc.,the proposed method has better results in Peak Signal-to-Noise Ratio(PSNR)and Structure self-SIMilarity(SSIM),speed,denoising effect and detail retention ability.