摘要
提出了一种结合像素分类与小波变换的图像去噪方法。首先用常用方法获得初步去噪图像,并将其分割为若干图像块,分别计算每个图像块的空间频率。利用归一化的空间频率,对不同的图像块采用不同的阈值进行去噪,空间频率高的图像块采用较小的阈值,反之采用较大阈值去噪。实验结果表明:该方法可在初步去噪图像的基础上进一步提高图像去噪的效果,同时较好地保持图像细节;其去噪效果优于常用的小波图像去噪方法,峰值信噪比(PSNR)相对常用方法最高可提高3.4 dB。
An adaptive image denoising scheme using pixel classification and wavelet transform is propose. At first, an initial denoised image is obtained by one of conventional image denoising methods. Then the image is partitioned into image blocks with the same size,and the spatial frequency of each image block is calculated. The different thresholds are employed to the image blocks according to the normalized spatial frequencies. The small threshold is used to the image block with the high spatial frequency, or the large threshold is employed. Experimental results show that this approach can reduce the image noise effectively,while little image detail is lost. This algorithm is superior to the conventional wavelet image denoising approaches with 3.4 dB improvement of peak signal noise rate （PSNR） at most.
出处
《光电子．激光》
EI
CAS
CSCD
北大核心
2007年第4期482-486,共5页
Journal of Optoelectronics·Laser
关键词
图像处理
图像去噪
像素分类
小波变换
image processing
image denoising
pixel classification
wavelet transform
