摘要
为解决Curvelet图像去噪所产生的"环绕"效应以及非局部TV模型去噪过度平滑而无法保持细小纹理的问题,本文提出了一种基于Curvelet变换与非局部TV模型相结合的图像去噪方法(Curvelet and Non-Local TV,CNL-TV)。该方法首先对含噪图像进行Curvelet变换,将其分解成不同尺度的图像;其次根据每层图像的特性,选择合适的非局部TV模型参数分别进行处理;最后将处理后的每层图像融合。实验结果表明,该算法不仅能够有效地减少噪声,消除Curvelet去噪产生的"环绕"效应,而且最大程度地保持了图像中的细小纹理成分。通过比较不同方法所得结果的峰值信噪比,验证了算法的有效性。
In order to solve the ＂wrap around＂ effect by Curvelet transform and the problem that non-local TV model can not maintain fine texture problem because of over smooth, a novel image denosing algorithm based on Curvelet transform and non-local TV model（CNL-TV） is proposed. The images are denoised by Curvelet transform firstly, which will be decomposed into different scales images, then the appropriate pa- rameters of non-local TV model are chose separately to treat images according to the characteristics of each level, and the images will be fused finally. The algorithm proposed in this paper could not only reduce noise effectively and eliminate the ＂wrap around＂ effect by Curvelet transform but also keep the fine tex- ture component as far as possible. The validity of the algorithm is verified by comparing the PSNR of re- sults with different methods.
出处
《青岛大学学报（自然科学版）》
CAS
2014年第1期59-65,共7页
Journal of Qingdao University(Natural Science Edition)
基金
国家自然科学基金(批准号:61170106)资助
