Introduction to Vision and to Image Processing. 2D Signals and Linear Systems. Sampling and Reconstruction: Uniform Sampling, Aliasing, General Sampling Grids. Quantization: Scalar, Visual Perception, Color Quantization. Image Enhancement: Gray Level Transformations, Histogram Processing, Spatial Filtering, Smoothing, Sharpening. Image Restoration: Maximum-Likelihood, Maximum a Posteriori. 2D Discrete Transforms. Multiresolution and Multi-Scale Representations and Analysis of Images. Image Compression: Fundamental Concepts of Information Theory, Image Redundancies, Error-Free Compression, Lossy Compression. Introduction to Computer Vision.