Model-based image segmentation

在许多图像处理和计算机视觉应用中，图像去噪和图像分割是一个至关重要的部分。去噪的目标是平滑图像的均匀区域，同时保持区域边界（即边缘）。同时，分割技术的目的是提取同质区域的边界。在传统的流水线去噪/分割方案中，分割步骤可能会由于去噪步骤或噪声过多而导致重要信息的丢失。与传统方法相比，将这两个步骤结合起来可以保留大部分丢失的信息。

Image de-noising and image segmentation represent a crucial part in many image processing and computer vision applications. The goal of de-noising is to smooth homogenous regions of an image while preserving the region boundaries (i.e., edges). Meanwhile, segmentation techniques aim to extract the boundaries of homogenous regions. In traditional pipelined de-noising/segmentation schemes, the segmentation step may suffer from a loss of vital information due to the de-noising step, or due to the excess of noise. Combining these two steps should preserve much of this lost information compared to the traditional methods.