论文标题
向后各向异性扩散和应用图像处理的高级离散化
High-Order Discretization of Backward Anisotropic Diffusion and Application to Image Processing
论文作者
论文摘要
各向异性扩散是数字图像处理（包括边缘检测和降解）中公认的工具。我们在这里提出了一个特定的非线性时间依赖性运算符，并为空间变量提供适当的高阶离散化。迭代过程强调了包围对象的轮廓线，以非常低的成本为准确的重建铺平了道路。这种方法的主要特征之一是可能依靠一组相当不变的不连续图像，该图像可以在不引入任何近似值的情况下确定边缘。
Anisotropic diffusion is a well recognized tool in digital image processing, including edge detection and denoising. We present here a particular nonlinear time-dependent operator together with an appropriate high-order discretization for the space variable. The iterative procedure emphasizes the contour lines encircling the objects, paving the way to accurate reconstructions at a very low cost. One of the main features of such an approach is the possibility of relying on a rather large set of invariant discontinuous images, whose edges can be determined without introducing any approximation.
