基于Demons算法改进的图像去噪模型研究

在Demons算法的基础上, 将扩散过程看作图像配准, 建立一种新的基于图像配准的Demons 去噪模型. 实验表明, 该模型去噪效果优于经典的Perona-Malik模型, 排除了模型的病态性. 考虑到新模型在图像去噪过程中仅靠梯度信息表示图像的局部特征还不完善, 故将水平集曲率作为控制图像结构的驱动力因素引入到此模型中, 提出了一种新的梯度和曲率双重驱动力的图像去噪模型. 分析和仿真结果表明, 两种新模型都可有效抑制噪声, 清晰度也有明显的提高, 其中双重驱动力的图像去噪模型去噪效果更具优越性.

Image denoising mo del based on the improved Demons algorithm

According to image registration, we build a new Demons model of image denoising, in which the diffusion access is regarded as image registration. The experimental results indicate that the performance of the model is better than that of the Perona-Malik model: the ill-condition of the model is removed. It is not enough to describe local characteristics only by using the gradient information in the access of image denoising, so a level set curvature which is the driving force of image structure controlling is introduced into the denoising model. Therefore we propose a new model of image denoising based on two driving forces of gradient and curvature. The simulation results show that the two improved models can both suppress noise effectively, their definitions are enhanced obviously, the performance of image denoising model of two driving forces is more greatly improved.