CNN-PDE非线性图像滤波器

偏微分(PDE)非线性图像滤波方法具有优良特性,但由于其计算量大而无法满足实时控制需求.细胞神经网(CNN)可以描述图像PDE模型,利用模拟CNN芯片并行求解,有助于提高其实时性.本文用CNN实现了PDE偏差非线性图像滤波器,提出了一种局部运算的噪声估计方法以选择适当的平滑系数.计算结果表明,这种噪声估计方法可以对不同噪声水平作出较精确的估计.仿真实验结果表明,CNN-PDE非线性滤波器取得了满意的滤波效果,用CNN实现PDE非线性滤波器的方法是有效可行的.

Image nonlinear filter based on CNN-PDE

An image nonlinear filter based on Partial Differential Equations (PDE) has good performance, but it consumes large time and resource. Cellular Neural Networks (CNN) can depict the spatial discrete PDE model, and by means of an CNN analog chip, CNN can solve PDE efficiently. A nonlinear filter based on CNN-PDE was studied, and for selecting the diffusion coefficient properly a noise-estimate technique was presented by means of local operation only. The test result showed that this noise-estimate technique offered a comparatively accurate measure of different noise levels. Simulations of artificial noise images showed that this CNN-PDE nonlinear filter would suppress noise and preserve image edge simultaneously. It is feasible and effective to realize the PDE image process technique by CNN.