图像和视频是人类记录、表达和传递外部世界的重要视觉载体，也是感知外部世界的视觉基础，图像处理是实现物联网、机器视觉和人工智能等相关应用的基本支撑技术。

  本课程将从如下八个方面来讲授图像处理的一些基本概念，方法与技术：

   1）图像表征（依据图像基信号是否基于数据驱动，介绍图像处理中经典的傅里叶变换，离散余弦变换和基于数据驱动的主元分析法，生动展现同一幅图像在不同变换下的形式）

   2）运动估计（分别介绍像素级别的光流法和图像块级别的块匹配算法原理，以及它们应用差异之所在）

   3）图像与视频压缩技术（分别介绍包括静态数字图像压缩标准JPEG和视频压缩MPEG原理）

   4）图像半色调技术（介绍包括最简单的阈值方法，用于印刷业的聚合型抖动模板和分散型抖动模板，误差传播法等）

   5）图像滤波技术（介绍图像中常见的噪声类型，传统图像滤波，如中值滤波和高斯滤波等，以及最近出现针对纹理的滤波方法）

   6）图像插值与超分辨率技术（介绍包括传统图像插值方法，和基于图像自相似图像超分辨率技术等）

   7）图像边缘检测与分割技术（介绍包括Canny算子，mean-shift图像色彩分割方法等）

   8）视频目标跟踪技术（介绍目前较热门的Discriminative Correlation Filter(DCF)的目标跟踪技术原理）。

在授课过程中我们通过理论与实践相结合方式，以及课后大量文献阅读来加深对图像处理基本概念和理论的理解；通过实例来分析比较不同图像处理方法的优缺点；通过提出问题来引导学生独立深入思考。

课程的目标是通过学习，能让学员掌握图像处理与计算机视觉中一些基本概念，基本研究思路和方法等，从而帮助他们展开相关领域后续深入的研究工作，和开发相关应用系统等。

线性代数，信号与系统，高等数学，统计概率论等。

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