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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/digital
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课程名称：数字图像与视频处理基础 概述：本课程将教您图像和视频处理的基本原理和工具，以及如何将这些知识应用于解决商业和科学领域的实际问题。数字图像和视频在科学（如天文学、生物医学）、消费、工业和艺术等众多应用中无处不在。它们覆盖了从可见光、红外到伽马射线等广泛的电磁波谱。因此，掌握图像和视频信号处理的能力对工程/科学学生、软件开发者和实践科学家来说是非常重要的。这门课程将涵盖图像和视频处理的基本知识，提供一个数学框架来描述和分析图像和视频作为二维和三维信号在空间、时空和频率域中的表现。 课程大纲： 1. **图像与视频处理简介**：学习图像和视频的二维和三维信号特性，以及其在电磁波谱上的变化及应用。 2. **信号与系统**：介绍二维信号和系统的基础知识，包括复杂指数信号、线性时不变系统、二维卷积和空间域过滤。 3. **傅里叶变换与采样**：探讨二维信号在频率域中的表现，包括二维傅里叶变换、采样、离散傅里叶变换和频率域过滤。 4. **运动估计**：涵盖运动估计和颜色表现与处理的应用，包括相位相关、块匹配、时空梯度法和颜色图像处理基础。 5. **图像增强**：研究图像和视频增强技术，包括点对点强度变换、直方图处理、线性与非线性噪声平滑、锐化和视频增强等。 6. **图像恢复：第一部分**：探讨图像和视频恢复的问题，包括图像恢复概述、逆过滤、约束最小二乘方法等。 7. **图像恢复：第二部分**：从随机角度研究图像和视频恢复，包括维纳恢复过滤、贝叶斯恢复算法等。 8. **无损压缩**：介绍图像和视频压缩，重点在无损压缩，包括信息理论要素、哈夫曼编码、算术编码等。 9. **图像压缩**：讨论有损图像压缩的基本方法，包括标量和向量量化、JPEG等。 10. **视频压缩**：分析视频压缩，重点是运动补偿混合视频编码和视频压缩标准如H.264、H.265等。 11. **图像与视频分割**：探讨图像和视频分割的问题及各种方法，包括基于强度不连续性和相似性的分割方法。 12. **稀疏性**：讨论稀疏性概念在图像和视频处理中的应用，包括稀疏性促进范数等。 通过本课程，您将掌握图像和视频处理的核心理论和实践技术，为在相关领域的应用打下坚实的基础。
Name:Introduction to Image and Video Processing
Description:In this module we look at images and videos as 2-dimensional (2D) and 3-dimensional (3D) signals, and discuss their analog/digital dichotomy. We will also see how the characteristics of an image changes depending on its placement over the electromagnetic spectrum, and how this knowledge can be leveraged in several applications.
Name:Signals and Systems
Description:In this module we introduce the fundamentals of 2D signals and systems. Topics include complex exponential signals, linear space-invariant systems, 2D convolution, and filtering in the spatial domain.
Name:Fourier Transform and Sampling
Description:In this module we look at 2D signals in the frequency domain. Topics include: 2D Fourier transform, sampling, discrete Fourier transform, and filtering in the frequency domain.
Name:Motion Estimation
Description:In this module we cover two important topics, motion estimation and color representation and processing. Topics include: applications of motion estimation, phase correlation, block matching, spatio-temporal gradient methods, and fundamentals of color image processing
Name:Image Enhancement
Description:In this module we cover the important topic of image and video enhancement, i.e., the problem of improving the appearance or usefulness of an image or video. Topics include: point-wise intensity transformation, histogram processing, linear and non-linear noise smoothing, sharpening, homomorphic filtering, pseudo-coloring, and video enhancement.
Name:Image Recovery: Part 1
Description:In this module we study the problem of image and video recovery. Topics include: introduction to image and video recovery, image restoration, matrix-vector notation for images, inverse filtering, constrained least squares (CLS), set-theoretic restoration approaches, iterative restoration algorithms, and spatially adaptive algorithms.
Name:Image Recovery : Part 2
Description:In this module we look at the problem of image and video recovery from a stochastic perspective. Topics include: Wiener restoration filter, Wiener noise smoothing filter, maximum likelihood and maximum a posteriori estimation, and Bayesian restoration algorithms.
Name:Lossless Compression
Description:In this module we introduce the problem of image and video compression with a focus on lossless compression. Topics include: elements of information theory, Huffman coding, run-length coding and fax, arithmetic coding, dictionary techniques, and predictive coding.
Name:Image Compression
Description:In this module we cover fundamental approaches towards lossy image compression. Topics include: scalar and vector quantization, differential pulse-code modulation, fractal image compression, transform coding, JPEG, and subband image compression.
Name:Video Compression
Description:In this module we discus video compression with an emphasis on motion-compensated hybrid video encoding and video compression standards including H.261, H.263, H.264, H.265, MPEG-1, MPEG-2, and MPEG-4.
Name:Image and Video Segmentation
Description:In this module we introduce the problem of image and video segmentation, and discuss various approaches for performing segmentation including methods based on intensity discontinuity and intensity similarity, watersheds and K-means algorithms, and other advanced methods.
Name:Sparsity
Description:In this module we introduce the notion of sparsity and discuss how this concept is being applied in image and video processing. Topics include: sparsity-promoting norms, matching pursuit algorithm, smooth reformulations, and an overview of the applications.
In this class you will learn the basic principles and tools used to process images and videos, and how to apply them in solving practical problems of commercial and scientific interests. Digital images and videos are everywhere these days – in thousands of scientific (e.g., astronomical, bio-medical), consumer, industrial, and artistic applications. Moreover they come in a wide range of the electromagnetic spectrum - from visible light and infrared to gamma rays and beyond. The ability to process image and video signals is therefore an incredibly important skill to master for engineering/science students, software developers, and practicing scientists. Digital image and video processing continues to enable the multimedia technology revolution we are experiencing today. Some important examples of image and video processing include the removal of degradations images suffer during acquisition (e.g., removing blur from a picture of a fast moving car), and the compression and transmission of images and videos (if you watch videos online, or share photos via a social media website, you use this everyday!), for economical storage and efficient transmission. This course will cover the fundamentals of image and video processing. We will provide a mathematical framework to describe and analyze images and videos as two- and three-dimensional signals in the spatial, spatio-temporal, and frequency domains. In this class not only will you learn the theory behind fundamental processing tasks including image/video enhancement, recovery, and compression - but you will also learn how to perform these key processing tasks in practice using state-of-the-art techniques and tools. We will introduce and use a wide variety of such tools – from optimization toolboxes to statistical techniques. Emphasis on the special role sparsity plays in modern image and video processing will also be given. In all cases, example images and videos pertaining to specific application domains will be utilized.
