Key Information
About the content
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.
Syllabus
- 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.
Instructors
Aggelos K. Katsaggelos
Joseph Cummings Professor
Department of Electrical Engineering and Computer Science
Content Designer
Northwestern University is a private research and teaching university with campuses in Evanston and Chicago, Illinois, and Doha, Qatar. Northwestern combines innovative teaching and pioneering research in a highly collaborative environment that transcends traditional academic boundaries.
Platform
Coursera is a digital company offering massive open online course founded by computer teachers Andrew Ng and Daphne Koller Stanford University, located in Mountain View, California.
Coursera works with top universities and organizations to make some of their courses available online, and offers courses in many subjects, including: physics, engineering, humanities, medicine, biology, social sciences, mathematics, business, computer science, digital marketing, data science, and other subjects.
An excellent course which make me feel myself proud. I wholeheartedly thank my professor for sharing his knowledge.Thank u sir i really enjoyed it
An excellent course which make me feel myself proud. I wholeheartedly thank my professor for sharing his knowledge.Thank u sir i really enjoyed it
Got to know the importance of Math, how math works in the background. Understood the basic programming with Matlab. Interesting concepts
As an amateur photographer who is interested in post-processing, I came here to find more about how image processing softwares work. Sometimes it took me lots of time to catch up what the professor was teaching. This course is not friendly to the person who does not have basic knowledge about signal processing and math. And the professor's accent is quite noticeable to me, a non-native English speaker, plus there are tons of errors and [UNKNOWN] in the subtitles, which is the one biggest challenges I had met. But frankly speaking, This course is great in the most of aspects, I have learnt a lot from it. The most of tests are relative easy compared to the lectures. BTW, In the final MATLAB test, there is a hint about normc function, that is useless for the student who is using MATLAB online because the function belongs to additional toolkits that online users will not have.
I am a newbie in image processing (from audio processing). I found this course interesting and I learnt a lot of new things. Thanks
Best part was the last part. Nice course :) Please try Tensorflow, Scikit, Octive with lots of open source support and volunteers. Big issue was obscure hidden code in matlab routines svd, qrd etc coupled with extremely aggressive Licensing of Matlab :(
