提出了一种新的结合非下采样Contourlet变换(NSCT)和斯坦无偏风险估计(SURE)的自适应图像去噪方法。通过NSCT对含噪图像进行分解, 根据斯坦无偏风险估计准则对分解后的噪声图像进行均方误差EMS估计, 并依据得到的EMS构造线性自适应阈值方程, 对含噪图像的每一个分解子带进行阈值去噪。对自适应阈值去噪后的图像分解子带进行重构, 得到去噪图像。实验结果表明, 该方法可以有效地消除标准图像和自然图像中的噪声, 在去噪图像峰值信噪比(PSNR)和边缘保持性能上都优于已有算法。

This paper presents a new adaptive image denoising scheme by combining the nonsubsampled contourlet transform (NSCT) and Stein’s unbiased risk estimation (SURE). The original image is first decomposed by using NSCT. Then the mean square error (EMS) is estimated based on Stein’s unbiased risk estimation. The noises of each decomposed subband are reduced by using the linear adaptive threshold function, which can be constructed based on the EMS. Finally, the denoised image is obtained after reconstructing the processed subbands. Experiments and comparisons on both standard images and natural images show that the proposed scheme can remove image noises effectively and outperforms the current schemes in regard of both the peak signal-to-noise-ratio (PSNR) and the edge preservation ability.