基于红-黑小波变换的图像去噪算法

Image Denoising Algorithm Based on The Red-Black Wavelet Transform

在红-黑小波变换的基础上,提出了一种新的阈值方法对小波系数进行萎缩以实现图像的去噪.该阈值方法结合了仿射阈值和非负Garrote阈值的优点,能在保留图像细节的基础上实现更好的去噪效果.通过仿真试验,对不同尺度下的阈值提出了一组最多适用到8级红-黑小波变换的阈值权重.结果证明,结合所提出的阈值方法和这一组阈值权重能实现较为理想的去噪效果.

Based on the red-black wavelet transform, a new thresholding for image denoising is proposed. Combing the characteristics of affine and non-negative thresholding, this method can keep a better balance between image details and it＇s denoising. After simulation on a multitude of images, eight different thresholding weights applied to as many as eight levels of red-black wavetlet transforms have been proposed. Simulaton proves that by combining the thresholding and the eight weights better results can be achieved.