中文摘要：

近年来K-SVD字典学习去噪算法因其耗时短、去噪效果好的特点得到广泛关注和应用，但该算法的适用条件为图像的噪声为加性噪声且噪声标准差已知。针对这一情况，先提出一种平滑图像块筛选方法，并将其与奇异值分解（singular value decomposition, SVD）相结合实现对图像的噪声标准差估计；再将得到的噪声估计方法与K-SVD字典学习去噪算法结合起来，提出一种具备噪声估计特性的K-SVD字典学习去噪算法。对多种图像的去噪实验结果表明，与Donoho小波软阈值去噪算法、全变分（total variation，TV）去噪算法相比，该算法不仅能够使去噪后图像的峰值信噪比提升1～3dB，并且能较好地保留图像的细节信息和边缘特征。

英文摘要：

In recent years, the K-SVD dictionary learning denoising algorithm has been widely concerned and applied because of its short time consuming and outstanding performance. But the application of this algorithm requires that the noise in image is additive noise and standard deviation of the noise is known. In view of this situation, this paper proposed a method to select the smooth image blocks and combined it with the singular value decomposition （SVD） to achieve the estimation of the noise standard deviation of the image. Then it proposed a new denoising algorithm which had the characteristic of noise estimation combining with the obtained noise estimation method and the K-SVD dictionary learning denoising algorithm. Experimental resuits of denoising some images show that, compared with Donoho wavelet soft threshold denoising algorithm and the total variation （TV） denoising algorithm, not only the peak signal to noise ratio（PSNR） of the image denoised by the proposed algorithm is improved by about 1 - 3 dB, but also the detailed information and edge features of the image can be better preserved.