Curvelet变换及其在图像去噪中的应用研究

Curvelet变换由小波变换发展而来,克服了小波变换在表达图像边缘的方向特性等方面的内在缺陷。本文阐述了Curvelet变换的基本原理,并结合实例采用改进的Curvelet变换阈值去噪算法进行了图像的去噪研究。采用的新阈值函数不但整体上连续性好,而且克服了硬阈值函数的不连续以及软阈值函数在处理较大Curvelet系数时总存在恒定偏差的不足,同时又保留了软、硬阔值函数原有的优点。实验结果表明本文算法无论是视觉效果还是客观标准上都优于传统算法。

Application of Curvelet transform in digit image denoising

Curvelet transform is developed from the wavelet transform and it has overcome some inherent limitations of wavelet in representing directions of image edge. In this paper, the basic principle of Curvelet is introduced. An improved thresholding function for image denoising is presented. This new threshold function not only can be continuous, but also can preserve the advantage of the soft-threshold and hard-threshold.The experiment shows that image denoising using this method performs better than those using traditional methods.