摘要： 采用传统非线性扩散图像去噪方法得到的图像边缘模糊，为此，提出一种有限自适应Shearlet域约束的极小化变分图像去噪算法。通过自适应阈值收缩Shearlet系数，保留图像纹理与边缘空间，利用全变差极小化平滑空间，建立全变差正则化的能量泛函去噪模型。实验结果表明，该算法能在减少图像噪声的同时，保留图像边缘信息，对含有丰富纹理结构的图像，去噪性能更佳。

关键词: 非线性扩散, Shearlet变换, 全变差, 图像去噪, 多尺度几何分析

Abstract: In this paper, an adaptive Shearlet domain regularized minimization total variation image denoising algorithm is proposed, which can overcome the problem of the traditional nonlinear diffusion image denoising methods. The texture and edge domain is adaptively shrinkaged in Shearlet domain. And then, a total variation regularized energy functional model with restrictions on the finite adaptive shearlet domain is used to deal with smooth domain. Experimental results show that this algorithm can reduce the noise and preserve the edge information, especially to the images containing abundant texture.

Key words: nonlinear diffusion, Shearlet transform, total variation, image denoising, multiscale geometric analysis

中图分类号: 

• TN919.73