Image processing is a crucial aspect of modern technology, with applications ranging from medical imaging to facial recognition. To master the intricacies of this field, a comprehensive understanding of the underlying concepts, algorithms, and tools is essential. Online courses provide a flexible and accessible platform for individuals seeking to bolster their image processing skills. With a plethora of options available, it can be challenging to identify the best courses that deliver high-quality education and practical experience. In this article, we explore the top online courses for image processing and highlight their unique features and benefits.
Here’s a look at the Best Image Processing Courses and Certifications Online and what they have to offer for you!
Image Processing Online Course Mit
- Image Processing Online Course Mit
- 1. DIP using MATLAB: Digital Image Processing for Beginners by TELCOMA Global 70,000+ Students! (Udemy) (Our Best Pick)
- 2. Learn Computer Vision and Image Processing in LabVIEW by Augmented Startups (Udemy)
- 3. Learn MATLAB with Image Processing from scratch! by Mohit Aggarwal (Udemy)
- 4. Python Digital Image Processing From Ground Up™ by Israel Gbati, PyTribe . (Udemy)
- 5. Complete Python Based Image Processing and Computer Vision by Minerva Singh (Udemy)
- 6. Learn image processing and GUIs while having fun in MATLAB by Mike X Cohen (Udemy)
- 7. Complete Python Image Processing Masterclass by Ashwin Pajankar • 80,000+ Students Worldwide (Udemy)
- 8. Practical Image Processing in C/C ++ From Ground Up™ by Israel Gbati, BHM Engineering Academy, PyTribe . (Udemy)
- 9. Complete Guide to Image Processing with MATLAB by Fawaz Sammani (Udemy)
- 10. Learning Path: OpenCV: Master Image Processing with OpenCV 3 by Packt Publishing (Udemy)
1. DIP using MATLAB: Digital Image Processing for Beginners by TELCOMA Global 70,000+ Students! (Udemy) (Our Best Pick)
The DIP using MATLAB course offered by TELCOMA Global is designed for beginners who want to learn how to create, process, communicate, and display digital images using computer algorithms. MATLAB is used extensively for DIP due to its high-performance language, powerful commands, and syntax. The main purpose of DIP is to improve the visual quality of images for a specific use. The course offers certification upon completion.Digital image processing methods offer several options for improving image quality, including image enhancement, segmentation, registration, and more. The selection of these methods depends on the imaging modality, job at hand, and viewing conditions. Algorithms can be used to transform signals from image sensors into digital images, eliminate noise and artifacts, prepare images for display or printing, and compress images for transfer across a network.The course covers a variety of topics, including M Function programming, intensity transformations, spatial filtering, frequency domain processing, image restoration and reconstruction, geometric transformations, color image processing, wavelets, morphological image processing, and image segmentation. The course offers a certification as proof of new skills learned.The course offers a 30-day money-back guarantee and lifetime access to course updates. The course content is divided into theory and practical sections.
The Learn Computer Vision and Image Processing in LabVIEW course, instructed by Augmented Startups, is designed for beginners seeking to build fully functional vision-based apps using LabVIEW and LabVIEW Vision Development Toolkit. The course covers basic concepts, tools, and functions, including installation of the LabVIEW Vision Development Toolkit, feature detection algorithms, object tracking, and optical character recognition. The course consists of 26 lectures and over 4 hours of content, with each chapter closing with exercises and the opportunity to develop your own vision-based apps. The course has received positive reviews with over 3040+ satisfied students enrolled, and includes working files, datasets, and code samples with a verifiable certificate of completion upon finishing the course. The course price will increase to $200 as of February 1st, 2019.
The course provides a foundation in image processing and computer vision which is a powerful and useful tool for beginning programmers. Learning these skills can lead to plentiful job opportunities in image processing, and can provide a strong background for picking up other computer vision tools such as OpenCV and Matlab. The course is easy to learn, well-documented, and provides a base for prototyping all types of vision-based algorithms.
The course takes students through multiple algorithms, including circle, color, and edge detection, as well as advanced feature detection such as pattern matching, object tracking, and barcodes. The course also covers theory behind each algorithm and how they are applied in real-world scenarios. The course concludes with the opportunity to create nine fully functional vision-based apps, including counting M&Ms in an image, color segmentation and tracking, coin blob detection, lane detection and ruler width measurement, pattern or template matching to detect complex objects, object tracking, bar code recognition, and optical character recognition (OCR).
The course provides a full Udemy 30 Day Money Back Guarantee if students are not satisfied.
This course titled Learn MATLAB with Image Processing from scratch! is offered by instructor Mohit Aggarwal. The course focuses on teaching students how to use the MATLAB toolbox for image processing. The course has received positive reviews from over 1000 students who have taken it. Reviews highlight the ease of understanding the complex subject matter and the ability to start working with the concepts immediately. The course is suitable for beginners as it starts from scratch with no prior MATLAB programming experience required.
The MATLAB Image Processing (IP) toolbox is widely used in academic institutions and enterprises due to its user-friendly organization and popularity. The course teaches the complete IP toolbox from scratch, starting with theoretical concepts and moving on to implementation with MATLAB programming. Supplementary materials such as presentation files and working MATLAB scripts are provided along with the lectures. The course covers beginner and intermediate level IP topics such as filtering, noise removal, morphological operations, histogram operations, thresholding, edge detection, and image segmentation. Quizzes are integrated into the course to monitor progress.
Real-world applications of IP are demonstrated through hands-on projects such as Detect the faces of all your friends in an image. The course is comprehensive and suitable for those who want to learn MATLAB for work or college. If a student is not satisfied with the course, a full refund is guaranteed. The course is broken down into sections such as getting started, basic reading and writing, image conversions, playing with image histogram, image smoothing, detecting edges, and more. The course concludes with a summary of the concepts covered.
The Python Digital Image Processing From Ground Up™ course, taught by Israel Gbati of PyTribe, covers a range of topics related to image processing, including edge-detection algorithms, convolution, filter design, gray-level transformation, and histograms. The course is designed to provide a solid foundation in practical image processing techniques using a programming-based approach, without relying on abstract mathematical theories. The course is available in different programming languages, with this version using Python.
By the end of the course, students will be able to perform 2-D discrete convolution with images in Python, perform edge-detection and spatial filtering, compute and equalize image histograms, perform gray level transformations, suppress noise in images, and understand various operators such as Laplacian, Sobel, Prewitt, and Robinson. Additionally, students will be equipped to give lectures on image processing. The course curriculum includes an introduction, setting up, Python essentials, basic image processing concepts and terminologies, geometric operations, image enhancement techniques, neighborhood processing, edge detection, image formation, an alternate setup for the Raspberry Pi, and closing.
This course is designed for individuals seeking practical image processing knowledge without relying on complex mathematical theory. The course is taught in a clear, straightforward language making it accessible for all individuals. The course is backed by a 30-day, full money-back guarantee.
This is a complete Python-based image processing and computer vision course that covers the important aspects of Keras and Tensorflow. The course is designed to help learners implement basic tasks using Jupyter Notebooks. The goal is to give learners a robust grounding in all aspects of data science within the Tensorflow framework. The course promises to help learners become proficient in Keras and Tensorflow, which can give them a competitive edge and boost their career to the next level.
The course instructor, Minerva Singh, is an Oxford University MPhil graduate with several years of experience in analyzing real-life data from different sources using data science-related techniques. Singh’s course provides a detailed introduction to using the powerful Python-driven framework for data science Anaconda for image processing and computer vision tasks. Learners will also be introduced to the relevant theoretical concepts, and how to install and use relevant packages including Tensorflow and Keras. The course covers eight complete sections addressing every aspect of Python-based image processing and computer vision, which includes detailed information on implementing machine learning algorithms, both supervised and unsupervised learning, creating artificial neural networks, transfer learning, and deep learning structures.
The course is designed to provide learners with hands-on methods to simplify and address difficult concepts related to image processing and computer vision. Singh uses real imagery data obtained from different sources to help learners implement the methods. After taking the course, learners will be able to use image processing and computer vision packages, such as OpenCV, along with gaining fluency in Tensorflow and Keras. The course is practical, with majority of the focus on implementing different techniques on real data and interpreting the results.
Overall, the course is aimed at learners who want to apply Python-based data science techniques on real image data into practice and start analyzing data for their own projects, regardless of their skill level. After each video, learners will learn a new concept or technique that they may apply to their own projects.
This course, titled Learn image processing and GUIs while having fun in MATLAB, aims to improve participants’ digital image processing and programming skills in MATLAB. The course is designed to be engaging, and enjoyable to participants who have no prior background in image processing. It includes a long description of the importance of digital images in various fields and how this course can enhance participants’ understanding of image processing and graphical user interfaces (GUIs).
The course offers fundamental skills in image processing and GUIs, and also aims to improve participants’ MATLAB programming skills. It covers numerical processing, control statements, working with data, and more. The prerequisites for the course include basic MATLAB programming experience, familiarity with variables, if-then statements, for-loops, and creating functions. Participants are advised to check the list of topics and preview videos to determine if the course is a good fit for them.
The course content is divided into various sections, including basic MATLAB image-processing programming, playing the Stoic Bird game, creating 3D magic-eye (autostereogram) pictures, and more. The course also includes a bonus section. Participants are encouraged to check out student reviews of the instructor’s other courses for insight into his teaching style.
7. Complete Python Image Processing Masterclass by Ashwin Pajankar • 80,000+ Students Worldwide (Udemy)
This course on Udemy, titled Complete Python Image Processing Masterclass, is designed to teach students about image processing and computer vision using Python 3. The course is taught by Ashwin Pajankar and has over 80,000 students enrolled worldwide. The course promises to equip students with one of the most lucrative skills sought by employers in the 21st century.
The course is comprehensive and covers everything from the basics of scientific Python ecosystem to advanced topics like feature detection and segmentation. It assumes no prior knowledge of image processing or Python and is suitable for beginners and advanced learners alike. The course comprises over 100 lectures, with more than 12 hours of video content, and comes with corresponding PDFs, image datasets, and Jupyter notebooks.
The course starts by helping students install Python3, NumPy, matplotlib, Jupyter, and Scikit-learn on their Windows computers and Raspberry Pi. It then delves into topics like the basics of digital image processing, ndarray manipulation, and statistical functions. The course includes a tour of the Python 3 environment on Raspberry Pi and covers Jupyter installation and basics, array creation routines, and basic visualization with Matplotlib.
Other topics covered in the course include transformations on images, histogram equalization, filtering, morphology, improving images, feature detection, segmentation, and miscellaneous operations on images. The course promises to teach students image processing in a practical manner, with each lecture accompanied by a programming video and a corresponding Jupyter notebook with Python 3 code.
The course offers lifetime access to the lectures, PDFs, image datasets, and Jupyter notebooks. The BONUS section of the course includes additional material for students who want to delve deeper into the topics covered. Overall, this course is a comprehensive and practical guide to image processing and computer vision using Python 3.
8. Practical Image Processing in C/C ++ From Ground Up™ by Israel Gbati, BHM Engineering Academy, PyTribe . (Udemy)
The Practical Image Processing in C/C++ From Ground Up™ course aims to provide students with a solid foundation in the most useful aspects of Image Processing in a programming-based approach. The course is designed to avoid obstacles of abstract mathematical theories by explaining image processing techniques in plain language. The course comes in different programming languages, and this version uses C++.
By the end of the course, students should be able to develop various algorithms, including 2-D Discrete Convolution, Edge-Detection, Spatial Filtering, Image Histogram and Equalization, and Gray Level Transformation. The course also covers topics such as noise suppression, operators (e.g., Laplacian, Sobel, Prewitt, Robinson), and image formation.
The course curriculum includes an introduction, setting up, basic image processing concepts and terminologies, arithmetic operations, histogram and equalization, geometric operations, gray level transformation, image enhancement techniques, edge detection, neighborhood processing, filter algorithms, and image formation.
The course is backed by a 30-day full money back guarantee, and students can choose to work with a programming language of their choice.
Enrolling in this course will help students gain practical image processing skills while avoiding complex mathematical theories, making it an engaging and easy-to-follow course.
The Complete Guide to Image Processing with MATLAB course, instructed by Fawaz Sammani, provides a comprehensive understanding of Image Processing and its application in MATLAB. The course covers the theories of Image Processing along with tutorials on Image Operations, Image Histograms, Image Filtering, Image Thresholding, Edge Detection in MATLAB, Image Morphology, Local Binary Patterns, and Practical Examples. Furthermore, the course presents an opportunity to apply the learned concepts in the creation of a Graphical User Interface (GUI) in MATLAB. The course includes friendly files to explain theoretical concepts and provides practices to test understanding and creativity.The course is divided into sections, which include the Introduction to MATLAB, Image Operations in MATLAB, Image Histograms, Image Filtering, Image Thresholding and Edge Detection, Image Morphology, Local Binary Patterns, Practical Examples in MATLAB, Creating a Complete Graphical User Interface in MATLAB, and Diving Deeper into Computer Vision.
The Learning Path titled OpenCV: Master Image Processing with OpenCV 3 by Packt Publishing provides a comprehensive guide to developing interactive computer vision applications using OpenCV 3’s C++ libraries. The course is designed to equip learners with the fundamental concepts of computer vision and image processing necessary for building computer vision applications. The Learning Path consists of a series of individual video products arranged in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
The Learning Path’s highlights include diving into the essentials of OpenCV, building one’s own projects, applying complex visual effects to images, reconstructing a 3D scene from images, and mastering the fundamental concepts of computer vision and image processing.
The Learning Path helps learners get started with the OpenCV library, learn how to install and deploy it to develop effective computer vision applications following good programming practices, read and display images, and understand the basic OpenCV data structures. Through the Learning Path, learners will start a new project, learn how to load an image file, handle keyboard events in the display window, interactively adjust image brightness, add a miniaturizing tilt-shift effect, blur images, and apply Instagram-like color ambiance filters to images.
At the end of the Learning Path, learners will have the skills and knowledge necessary to build computer vision applications that make the most of OpenCV 3.
The Learning Path is taught by two renowned experts, Robert Laganiere and AdiShavit. Robert is a professor at the School of Electrical Engineering and Computer Science of the University of Ottawa, Canada. He authored the OpenCV2 Computer Vision Application Programming Cookbook in 2011 and co-authored Object Oriented Software Development, published by McGraw Hill in 2001. AdiShavit is an experienced software architect and has been an OpenCV user since it was in early beta back in 2000.