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Learn Image Processing, earn certificates with paid and free online courses from Stanford, MIT, Johns Hopkins, Yale and other top universities around the world. Read reviews to decide if a class is right for you.
Apply your Python skills to take on image data! From pre-processing to deep learning, you'll discover the many ways you can leverage image data.
The MOOC teaches the basics of industrial image processing - the hardware components, common algorithms and applications.
Explore edge detection techniques in image processing, from basic concepts to advanced algorithms like Canny Edge Detector, with practical applications and evaluation methods.
Explore image processing techniques including filtering, convolution, and edge detection. Learn about derivatives, noise reduction, and various edge detection algorithms for computer vision applications.
Explore image processing fundamentals, including digitization, histograms, noise types, and filtering techniques. Learn about derivatives, averages, and correlation in the context of digital image analysis.
Explore image pyramids, including Gaussian and Laplacian types, their applications in image processing, and techniques like reduction, expansion, and compression.
Explore image feature extraction, line fitting techniques, and the Hough Transform for shape detection in computer vision, including practical applications and challenges.
Explore the foundations and cutting-edge applications of digital media, from image processing and computer vision to game development and network security technologies.
A complete high dynamic range workflow, from assembly to adjustment to finishing touches.
Explore advanced techniques in blind image deblurring, including surface-aware strategies and Quaternion-based methods for color image restoration, with applications in text and natural image processing.
Explore joint estimation and contour detection in large-scale image processing, focusing on textured image segmentation and image restoration using advanced optimization techniques.
Exploring space-variant regularisation models for inverse imaging problems, focusing on Total Variation regularisation and its adaptive improvements for local image structures.
Explore perceptual hashing techniques for identifying visual similarities between images, with practical applications and insights shared by Jan Stępień.
Discover how to reverse engineer a string art algorithm using Python, covering greedy algorithms, image processing, and performance optimization.
Explore the differences, pros, and cons between DNG and native RAW file formats. Learn when to convert and how to make informed decisions for your photography workflow.
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