压缩感知的人脸图像去噪

Face Image Denoising of Compressed Sensing

为解决人脸识别领域的噪声图像恢复问题,提出一种压缩感知的人脸图像去噪算法,协同稀疏性度量(collaborative sparse measure,CSM).CSM算法利用图像的先验知识,用一个域将图像稀疏表示,将图像的二维稀疏表示和三维稀疏表示同时进行自适应混合空间域转换,利用增广拉格朗日技术求解.实验结果表明,CSM算法的信噪比明显高于传统算法的信噪比,具有高效性.

To solve the problem of noisy image restoration in face recognition area, a algorithm ot race image de-noising of compressed sensing was proposed in this paper, called Collaborative Sparse Measure （CSM） . The algorithm of CSM used a priori knowledge of image, image sparse representation with a domain, and the sparse representation of two-dimensional and three-dimensional simultaneously did adaptive hybrid spa- tial domain conversion, and solved problem with Augmented Lagrangian technique. The result of the experi- ment indicates that the signal to noise ratio of CSM algorithm is superior to that of the traditional algorithm, which has a high efficiency.