Fundamentals of Digital Image and Video Processing

课程
en
英语
72 时
此内容评级为 4.3519/5
来源
  • 来自www.coursera.org
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  • 12 序列
  • 等级 介绍

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课程详情

教学大纲

  • Week 1 - Introduction to Image and Video Processing
    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 spect...
  • Week 2 - Signals and Systems
    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.
  • Week 3 - Fourier Transform and Sampling
    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.
  • Week 4 - Motion Estimation
    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 pro...
  • Week 5 - Image Enhancement
    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...
  • Week 6 - Image Recovery: Part 1
    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 appro...
  • Week 7 - Image Recovery : Part 2
    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 algorith...
  • Week 8 - Lossless Compression
    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 cod...
  • Week 9 - Image Compression
    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.
  • Week 10 - Video Compression
    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.
  • Week 11 - Image and Video Segmentation
    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 advanc...
  • Week 12 - Sparsity
    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.

先决条件

没有。

讲师

Aggelos K. Katsaggelos
Joseph Cummings Professor
Department of Electrical Engineering and Computer Science

编辑

西北大学是一所美国大学,位于美国伊利诺伊州埃文斯顿(芝加哥北部)。它是世界上最负盛名的大学之一,尤其是在新闻学、经济学和戏剧方面。该大学有两个校区,一个位于埃文斯顿(主校区),另一个位于芝加哥市中心。

平台

Coursera是一家数字公司,提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。

Coursera与顶尖大学和组织合作,在线提供一些课程,并提供许多科目的课程,包括:物理,工程,人文,医学,生物学,社会科学,数学,商业,计算机科学,数字营销,数据科学 和其他科目。

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