摘要
为了解决已有图像去噪算法中出现的伪吉布斯振荡和模糊现象,本文利用图像的稀疏表示理论提出一种新的Shearlet域图像去噪算法。该算法是对图像整体去噪:首先对噪声图像进行Shearlet变换,然后利用稀疏表示模型构造出去噪的最优化模型,进行稀疏编码,并加入迭代阈值的方法求解,从而获得干净的Shearlet系数,最后重构图像得到去噪后的图像。实验结果表明:提出的方法不仅PSNR上能够提高3d B,而且MSSIM也达到提高了0.03,使得视觉效果更优,纹理细节和边缘信息得到保证。
In order to solve the pseudo-Gibbs oscillation and fuzzy phenomena in the existing image denoising algorithms,in this paper,a new image denoising algorithm is proposed by using the theory of image sparse representation in Shearlet domain.The proposed algorithm is to denoise image from the entire image information： firstly,Shearlet transform is applied to the noisy image,then,the sparse representation model is used to construct the denoising optimization model.Then performed the sparse coding,and the added iterative threshold to obtain the clean Shearlet coefficients.The experiment results show that the proposed method not only increase 3 d B in PSNR,but also increase 0.03 in MSSIM,makes the visual effect better,and guarantees texture details and edge information.
出处
《激光杂志》
北大核心
2017年第10期96-100,共5页
Laser Journal
基金
江苏省"六大人才高峰"高层次人才项目资助
江苏省"333高层次人才培养工程"资助
江苏高校优势学科Ⅱ期建设工程资助项目
