Iterative regularization method for image denoising with adaptive scale parameter

一种变尺度参数的迭代正则去噪算法(英文)

In order to decrease the sensitivity of the constant scale parameter, adaptively optimize the scale parameter in the iteration regularization model （IRM） and attain a desirable level of applicability for image denoising, a novel IRM with the adaptive scale parameter is proposed. First, the classic regularization item is modified and the equation of the adaptive scale parameter is deduced. Then, the initial value of the varying scale parameter is obtained by the trend of the number of iterations and the scale parameter sequence vectors. Finally, the novel iterative regularization method is used for image denoising. Numerical experiments show that compared with the IRM with the constant scale parameter, the proposed method with the varying scale parameter can not only reduce the number of iterations when the scale parameter becomes smaller, but also efficiently remove noise when the scale parameter becomes bigger and well preserve the details of images.

为了降低迭代正则化中定尺度参数对快速收敛的敏感性、自适应地优化尺度参数并提高其去噪效果,提出了一种变尺度参数的迭代正则化去噪算法.首先,修改了经典的正则化项,并推导出尺度参数公式;然后,通过研究迭代次数与尺度参数序列的变化趋势,得到变尺度参数的初始值;最后,进行正则化去噪.数值实验表明:相对于恒定尺度参数的IRM算法,变尺度参数IRM算法比选取尺度参数偏小的IRM算法迭代次数大大减少;比选取尺度参数偏大的IRM算法去噪效果更为明显,并较好地保持了图像的细节.