CAJ | 학술논문

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.
위료강저질대정칙화중정척도삼수대쾌속수렴적민감성、자괄응지우화척도삼수병제고기거조효과,제출료일충변척도삼수적질대정칙화거조산법.수선, 수개료경전적정칙화항,병추도출척도삼수공식;연후,통과연구질대차수여척도삼수서렬적변화추세,득도변척도삼수적초시치;최후,진행정칙화거조.수치실험표명:상대우항정척도삼수적IRM산법,변척도삼수IRM산법비선취척도삼수편소적IRM산법질대차수대대감소;비선취척도삼수편대적IRM산법거조효과경위명현,병교호지보지료도상적세절.
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.