摘要
针对现有图像去噪算法去噪效率与信号保真度不高的现象,通过研究小波变换与Contourlet变换,将其有机的结合在一起从而实现优势互补,并提出一种高效的阈值去噪算法,通过建立最大值列表,引入适当的阈值将其系数进行分类,并使用优化后的软阈值去噪算法与边缘优化算法对其分类处理,实验表明,该算法能够有效的对含噪图像进行去噪的同时保留其边缘信息,具有高效性、保真度高的图像去噪特性,在图像去噪领域有较好的发展前景。
In order to improve the de-noising efficiency and heighten the signal fidelity of the existing image de-noising algorithms, the wavelet transform and eontourlet transform are studied, the complementary advantages are achieved by combining the two algorithms. An efficient threshold with de-noising algorithm is proposed. Through the establishment of a maximum value list, an appropriate threshold is introduced, and its coefficient is classified, at last, the images are processed by using the optimized soft threshold de-noising algorithm and edge optimization algorithm. Experiments show that the algorithms can effectively de-noise and preserve the edge information. It have high efficiency and high-fi- delity.
出处
《激光与红外》
CAS
CSCD
北大核心
2013年第7期831-836,共6页
Laser & Infrared
