Northwestern University - Fundamentals of Digital Image and Video Processing
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Fundamentals of Digital Image and Video Processing at Coursera Overview
Duration | 36 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Fundamentals of Digital Image and Video Processing at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Approx. 36 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Fundamentals of Digital Image and Video Processing at Coursera Course details
- 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.
Fundamentals of Digital Image and Video Processing at Coursera Curriculum
Introduction to Image and Video Processing
Analog v.s. Digital Signals
Image and Video Signals
Electromagnetic Spectrum
Welcome Class!
Grading Policy
Further Reading
About Us
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Homework 1
Signals and Systems
2D and 3D Discrete Signals
Complex Exponential Signals
Linear Shift-Invariant Systems
2D Convolution
Filtering in the Spatial Domain
MATLAB
Use of MATLAB for Programming Assignments
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Homework 2
Fourier Transform and Sampling
2D Fourier Transform
Sampling
Discrete Fourier Transform
Filtering in the Frequency Domain
Change of Sampling Rate
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Homework 3
Motion Estimation
Applications of Motion Estimation
Phase Correlation
Block Matching
Spatio-Temporal Gradient Methods
Fundamentals of Color Image Processing
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Homework 4
Image Enhancement
Introduction
Point-wise Intensity Transformations
Histogram Processing
Linear Noise Smoothing
Non-linear Noise Smoothing
Sharpening
Homomorhpic Filtering
Pseudo Coloring
Video Enhancement
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Homework 5
Image Recovery: Part 1
Examples of Image and Video Recovery
Image Restoration
Matrix-Vector Notation for Images
Inverse Filtering
Constrained Least Squares
Set-Theoretic Restoration Approaches
Iterative Restoration Algorithms
Iterative Least-Squares and Constrained Least-Squares
Spatially Adaptive Algorithms
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Homework 6
Image Recovery : Part 2
Wiener Restoration Filter
Wiener v.s. Constrained Least-Squares Restoration Filter
Wiener Noise Smoothing Filter
Maximum Likelihood and Maximum A Posteriori Estimation
Bayesian Restoration Algorithms
Other Restoration Applications
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Homework 7
Lossless Compression
Introduction
Elements of Information Theory - Part I
Elements of Information Theory - Part II
Huffman Coding
Run-Length Coding and Fax
Arithmetic Coding
Dictionary Techniques
Predictive Coding
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Homework 8
Image Compression
Scalar Quantization
Vector Quantization
Differential Pulse-Code Modulation
Fractal Image Compression
Transform Coding
JPEG
Subband Image Compression
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Homework 9
Video Compression
Motion-Compensated Hybrid Video Encoding
On Video Compression Standards
H.261, H.263, MPEG-1 and MPEG-2
MPEG-4
H.264
H.265
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Homework 10
Image and Video Segmentation
Methods Based on Intensity Discontinuity
Methods Based on Intensity Similarity
Watersheds and K-Means Algorithms
Advanced Methods
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Homework 11
Sparsity
Introduction
Sparsity-Promoting Norms
Matching Pursuit
Smooth Reformulations
Applications
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Homework 12
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