Digital Image and Video Processing Essentials
Master fundamental principles and tools for processing images and videos. Learn practical applications in various fields.
Master fundamental principles and tools for processing images and videos. Learn practical applications in various fields.
This comprehensive course covers the fundamentals of digital image and video processing, providing students with both theoretical knowledge and practical skills. Over 12 modules, participants will explore a wide range of topics, from basic principles of 2D and 3D signals to advanced concepts like sparsity in image processing. The course begins with an introduction to image and video signals, then progresses through key areas such as Fourier transforms, motion estimation, image enhancement, and compression techniques. Students will learn about both lossless and lossy compression methods, including popular standards like JPEG and MPEG. The curriculum also covers image recovery, segmentation, and the application of sparsity in modern image processing. Through a combination of video lectures, readings, and hands-on assignments using MATLAB, students will gain practical experience in solving real-world image and video processing problems.
4.6
(1,729 ratings)
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English
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What you'll learn
Understand the fundamentals of 2D and 3D signals in image and video processing
Master Fourier transforms and their applications in image analysis
Learn motion estimation techniques for video processing
Explore image enhancement methods for improving visual quality
Understand image recovery techniques for restoration and denoising
Gain knowledge of lossless and lossy compression algorithms
Skills you'll gain
This course includes:
23 Hours PreRecorded video
12 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 12 modules in this course
This course provides a comprehensive introduction to the field of digital image and video processing. It covers fundamental concepts and practical applications across various domains. The curriculum is structured to build a strong foundation in signal processing theory before delving into specific image and video processing techniques. Students will learn about 2D and 3D signals, Fourier transforms, and sampling before exploring more advanced topics like motion estimation, image enhancement, and compression. The course also covers cutting-edge areas such as image recovery, segmentation, and the application of sparsity in image processing. Throughout the course, students will gain hands-on experience using MATLAB to implement various algorithms and techniques. By the end of the course, participants will have a solid understanding of both the theoretical principles and practical applications of digital image and video processing, preparing them for further study or application in fields ranging from multimedia technology to scientific imaging.
Introduction to Image and Video Processing
Module 1 · 2 Hours to complete
Signals and Systems
Module 2 · 2 Hours to complete
Fourier Transform and Sampling
Module 3 · 2 Hours to complete
Motion Estimation
Module 4 · 2 Hours to complete
Image Enhancement
Module 5 · 3 Hours to complete
Image Recovery: Part 1
Module 6 · 3 Hours to complete
Image Recovery : Part 2
Module 7 · 2 Hours to complete
Lossless Compression
Module 8 · 3 Hours to complete
Image Compression
Module 9 · 3 Hours to complete
Video Compression
Module 10 · 3 Hours to complete
Image and Video Segmentation
Module 11 · 2 Hours to complete
Sparsity
Module 12 · 3 Hours to complete
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Instructor
Renowned Expert in Multimedia Signal Processing at Northwestern University.
Professor Aggelos Katsaggelos has been a faculty member at Northwestern University since 1985, currently serving as the Joseph Cummings Professor in the Department of Electrical Engineering and Computer Science. He is also the Director of the Motorola Center for Seamless Communications and holds various appointments, including being part of the Academic Affiliate Staff at NorthShore University Health System and affiliated faculty in the Department of Linguistics at Northwestern. He obtained his Diploma in electrical and mechanical engineering from Aristotelian University of Thessaloniki in 1979, followed by an M.S. and a Ph.D. in electrical engineering from Georgia Institute of Technology in 1981 and 1985, respectively. His research interests encompass multimedia signal processing, computer vision, machine learning, and more. With an extensive publication record that includes five books, over 210 journal papers, and numerous conference papers and patents, he has supervised 70 M.S. and 46 Ph.D. students. A Fellow of IEEE and SPIE, he has received multiple awards for his contributions to the field and has served as Editor-in-Chief of the IEEE Signal Processing Magazine for six years.
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4.6 course rating
1,729 ratings
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.