DOI码：

10.1109/FSKD.2012.6234041

所属单位：

IEEE Computer Society

发表刊物：

Proceedings - 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012

摘要：

The sparse and redundant representations of signal theory have aroused extensive and deep research in recent years, and been widely applied to image processing. Aiming to study the performance and suitability of the sparse and redundant representations in image denoising, our paper introduces a sparse and redundant representations algorithm based on over-complete learned dictionary to process different types of images. We use the K-SVD denoising framework and modify its initial dictionary, and then mainly focus on using it to study its denoising performance and suitability for different types of images, and then compare it with some other image denoising algorithms. As to the remote sensing images denoising, the experiment results show that the K-SVD algorithm can leads to the state-of-art denoising performance at low noisy levels, but for high noisy levels, its performance isn't good on PSNR and visual effect, that is it cannot retain the local details of images.

合写作者：

Du Bo,Hong Cheng,Wang Haofeng,Zhang Dengyi

第一作者：

Su Kehua

论文类型：

期刊论文

通讯作者：

Fu Hongbo

文献类型：

J

页面范围：

395-398

是否译文：

否

发表时间：

2012-01-01