Digital Image Processing
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 8.86 GB | Duration: 7h 9m
Image Enhancement, Image Filtering
What you’ll learn
Explain the fundamentals of Digital Image Processing and its methodologies
Applying spatial and frequency filters for Image enhancement
Design and implement various Image restoration filters
Apply deep learning algorithms for image quality improvement
Requirements
No prerequisite
Description
Dive into the fascinating world of Digital Image Processing and learn how computers interpret, analyze, and enhance visual information. This course provides a strong foundation in image formation, image representation, color models, filtering, enhancement, segmentation, feature extraction, and computer vision techniques. Students will understand how digital images are created, stored, processed, and used in real-world applications.The course covers important concepts such as RGB and HSV color spaces, spatial and frequency domain processing, edge detection, morphological operations, image restoration, object recognition, and pattern analysis. It also introduces modern applications of Artificial Intelligence and Deep Learning in image processing. Through practical examples and real-world case studies, learners will gain insights into medical image analysis, satellite image processing, autonomous systems, multimedia applications, robotics, and facial recognition technologies.Designed in a simple and structured manner, this course is suitable for undergraduate students, postgraduate learners, researchers, engineers, educators, and AI enthusiasts who want to build expertise in image processing and computer vision. No advanced background is required-concepts are explained from basics to advanced levels with clarity and practical relevance.By the end of this course, learners will be able to understand image processing fundamentals, apply image enhancement and analysis techniques, and develop confidence in solving real-world visual computing problems using modern computational approaches.
Undergraduate Engineering students
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