Fundamentals of Digital Image and Video Processing

Go to Course: https://www.coursera.org/learn/digital

Introduction

### Course Review: Fundamentals of Digital Image and Video Processing on Coursera As we navigate through an increasingly digital world, the significance of image and video processing cannot be overstated. The **Fundamentals of Digital Image and Video Processing** course on Coursera offers an insightful and comprehensive foundation in this vital field. Here’s an in-depth look at the course, what it encompasses, and why it deserves your attention. #### Course Overview In a nutshell, this course aims to equip learners with the essential principles and tools used in image and video processing. With the widespread use of digital images and videos in various domains—ranging from scientific research (like astronomy and biomedical imaging) to commercial applications and artistic expressions—understanding how to manipulate and process these visual data types is not just useful, but increasingly necessary. #### Syllabus Breakdown The course is structured into multiple modules that progressively build your understanding: 1. **Introduction to Image and Video Processing**: - Familiarizes students with basic concepts and the different types of signals in images and videos. 2. **Signals and Systems**: - Covers the fundamental theory behind 2D signals, exploring convolution and spatial filtering. 3. **Fourier Transform and Sampling**: - Introduces students to the frequency domain, sampling theories, and main concepts like the 2D Fourier Transform. 4. **Motion Estimation**: - Discusses techniques for motion detection and the processing of color images. 5. **Image Enhancement**: - Focuses on improving image quality through various enhancement techniques, including noise reduction and sharpening methods. 6. **Image Recovery (Parts 1 & 2)**: - Explores methods for restoring images and videos through advanced algorithms and stochastic approaches. 7. **Lossless Compression**: - Introduces essential compression techniques, emphasizing lossless methods crucial for data integrity. 8. **Image Compression**: - Delves into lossy compression techniques often encountered in real-world applications, such as JPEG. 9. **Video Compression**: - Discusses standards and techniques essential for efficient video coding. 10. **Image and Video Segmentation**: - Investigates various techniques for isolating significant parts of images and videos, crucial for different applications like object recognition. 11. **Sparsity**: - Introduces a modern perspective on processing, focusing on the importance of sparsity in improving image and video analysis. #### What Makes This Course Stand Out? - **Comprehensive Content**: The course is designed for both beginners and those with some prior knowledge in digital processing. Each module systematically builds on previous concepts, creating a cohesive learning journey. - **Practical Applications**: Ranging from commercial to scientific venues, the principles taught can be directly applied to real-world projects, making the learning highly relevant. - **Expert Instruction**: The course is often taught by industry professionals or academia, ensuring that students receive insights from experienced practitioners in the field. - **Flexibility of Learning**: Being an online course, students can learn at their own pace, making it accessible to those currently working or studying full-time. #### Recommendation I highly recommend the **Fundamentals of Digital Image and Video Processing** course on Coursera for anyone interested in entering the field of digital media. Whether you're a student in computer science, someone in a creative field, or a professional seeking to expand your skillset, this course offers invaluable insights and practical tools to enhance your understanding of digital image and video processing. By the end of the course, you will not only have a solid grasp of theoretical concepts but also the ability to apply various image processing techniques to tackle practical problems, enhancing your competence in this rapidly evolving area. Enroll now, and take the first step towards mastering digital image and video processing!

Syllabus

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 spectrum, and how this knowledge can be leveraged in several applications.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.