基于梯度与曲率相结合的图像平滑模型的研究

Image smoothing model based on the combination of the gradient and curvature

在图像处理过程中,为了在图像去噪时更好地保留图像边缘细节信息,首先结合扩散系数和曲率的性质建立了一个曲率平滑模型.考虑到图像受到噪声污染时曲率会发生显著变化,将图像的水平集曲率作为一个检测因子代入到上述模型中,提出了一个梯度与曲率相结合的新模型.分析与仿真结果表明,该模型与Perona-Malik模型相比较保留了更多的图像信息,有效地增强了图像尖锐的边缘,同时很好地保持了图像的直线和曲线边缘、角点、斜坡和小尺度特征,是一个理想的模型.

In image processing, in order to keep the detailed information about image edge, we propose a curvature smoothing model based on the nature of diffusion coefficient and curvature. Considering the fact that the curvature will change significantly when the image is affected by noise pollution, in this article we will continue to take the level set curvature as a detection factor and substitute it into the model, then we present a new model which combines gradient and curvature. Analysis and simulation indicate that the new model can keep more image information than the Perona-Malik model, and it can strengthen the sharp edge of the image efficiently, and well keep the straight lines of image, and edges, corners, slopes and small-scale features of curve at the same time, so this model is an ideal model.