Digital Image Processing (MCA334) crash course, Online MCA, Semester 3:
This subject covers how digital images are formed, enhanced in the spatial and frequency domains, restored, compressed, segmented, described and recognised.
- 17 video lessons
- 4h 15m
- Study notes
- 85 quiz questions
- Assignment
17 lessons in Digital Image Processing
Each lesson pairs one topic with a hand-picked video, trimmed to the part that teaches it.
About this subject
This subject covers how digital images are formed, enhanced in the spatial and frequency domains, restored, compressed, segmented, described and recognised. After it you will be able to choose and apply suitable filtering, restoration, compression, segmentation and morphological techniques to grayscale and colour images.
- 1
Introduction to Digital Image Processing
Module I, Introduction and Digital Image Fundamentals: The origins of Digital Image Processing, Examples of Fields that Use Digital Image Processing, Fundamentals Steps in Image Processing, Elements of Digital Image Processing Systems
Digital Image Processing - Introduction to Digital Image Processing - Image Processing· Ekeeda· 22 min
Study notes 5 quiz questionsWatch lesson - 2
Image Sampling and Quantization
Module I, Introduction and Digital Image Fundamentals: Image Sampling and Quantization
sampling and quantization in digital image processing· Perfect Computer Engineer· 9 min
Study notes 5 quiz questionsWatch lesson - 3
Pixel Relationships: Neighbours, Connectivity and Distance
Module I, Introduction and Digital Image Fundamentals: Some basic relationships like Neighbours, Connectivity, Distance Measures between pixels, Linear and Non Linear Operations
Relationship between Pixels in Digital Image Processing: Neighborhood, Adjacency & Distance measures· Study with Dr. Dafda· 22 min
Study notes 5 quiz questionsWatch lesson - 4
Gray Level Transformations
Module II, Image Enhancement in the Spatial Domain: Some basic Gray Level Transformations
Gray level transformation in digital image processing in hindi language. Ch-1 Lecture-8· Education Arena· 16 min
Study notes 5 quiz questionsWatch lesson - 5
Histogram Equalization and Processing
Module II, Image Enhancement in the Spatial Domain: Histogram Processing, Enhancement Using Arithmetic and Logic operations
Histogram Equalization in DIP | Solved Numerical Example for Image Enhancement· EC Academy· 10 min
Study notes 5 quiz questionsWatch lesson - 6
Spatial Filters for Smoothing and Sharpening
Module II, Image Enhancement in the Spatial Domain: Basics of Spatial Filters, Smoothening and Sharpening Spatial Filters, Combining Spatial Enhancement Methods
Smoothing Spatial Filters in digital image processing· College Friendly· 10 min
Study notes 5 quiz questionsWatch lesson - 7
Fourier Transform and Frequency Domain Filtering
Module III, Image Enhancement in the Frequency Domain: Introduction to Fourier Transform and the frequency Domain, Smoothing and Sharpening Frequency Domain Filters, Homomorphic Filtering
Frequency domain filtering in image processing - Low pass and High pass filters· College Friendly· 24 min
Study notes 5 quiz questionsWatch lesson - 8
Image Degradation Model and Noise Models
Module III, Image Enhancement in the Frequency Domain: Image Restoration: A model of The Image Degradation / Restoration Process, Noise Models, Restoration in the presence of Noise Only Spatial Filtering, Periodic Noise Reduction by Frequency Domain Filtering
Image Restoration & Degradation Model in Digital Image Processing (DIP) | EC Academy· EC Academy· 5 min
Study notes 5 quiz questionsWatch lesson - 9
Estimating the Degradation Function and Inverse Filtering
Module III, Image Enhancement in the Frequency Domain: Linear Position-Invariant Degradations, Estimation of Degradation Function, Inverse filtering
L29 | Estimation of Degradation Function | Direct & Indirect || Digital Image Processing (AKTU)· Dr. Sapna Katiyar· 14 min
Study notes 5 quiz questionsWatch lesson - 10
Wiener and Constrained Least Squares Filtering
Module III, Image Enhancement in the Frequency Domain: Wiener filtering, Constrained Least Square Filtering, Geometric Mean Filter, Geometric Transformations
38. Digital Image Processing: Wiener v s Constrained Least Squares Restoration Filter· Cybersecurity With Shahid· 15 min
Study notes 5 quiz questionsWatch lesson - 11
Image Compression: Redundancy and Models
Module IV, Image Compression: Coding, Interpixel and Psychovisual Redundancy, Image Compression models, Elements of Information Theory
L39 | Introduction to Redundancy | Types of Redundancy || Digital Image Processing (AKTU)· Dr. Sapna Katiyar· 23 min
Study notes 5 quiz questionsWatch lesson - 12
Lossless and Lossy Compression and Standards
Module IV, Image Compression: Error free comparison, Lossy compression, Image compression standards
Lossless compression and lossy compression in digital image processing | Lec-28· Er Sahil ka Gyan· 5 min
Study notes 5 quiz questionsWatch lesson - 13
Edge Detection and Edge Linking
Module IV, Image Compression: Image Segmentation: Detection of Discontinuities, Edge linking and boundary detection
Edge Detection in Image Processing 🔥 🔥· Perfect Computer Engineer· 11 min
Study notes 5 quiz questionsWatch lesson - 14
Thresholding and Region-Based Segmentation
Module IV, Image Compression: Threshold, Region Oriented Segmentation, Motion based segmentation
Region based Segmentation· College Friendly· 24 min
Study notes 5 quiz questionsWatch lesson - 15
Boundary and Regional Descriptors
Module V, Representation and Description: Representation, Boundary Descriptors, Regional Descriptors, Use of Principal Components for Description
Boundary Descriptors in Image Description for DIP and its implementation in MATLAB || Description||· Study with Dr. Dafda· 13 min
Study notes 5 quiz questionsWatch lesson - 16
Morphological Image Processing
Module V, Representation and Description: Introduction to Morphology, Some basic Morphological Algorithms
Morphological Operations· College Friendly· 14 min
Study notes 5 quiz questionsWatch lesson - 17
Object Recognition and Pattern Classification
Module V, Representation and Description: Object Recognition: Patterns and Pattern Classes, Decision-Theoretic Methods, Structural Methods
Patterns and Pattern Classes - Object Recognition - Image Processing and Machine Vision· Ekeeda· 18 min
Study notes 5 quiz questionsWatch lesson
Assignment
Digital Image Processing Problem Set: From Pixels to Recognition
Less scrolling. More passing.
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Hand-picked video lessons
17 short lessons, each with the best YouTube explanation we could find, trimmed to the part that matters.
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Study notes
17 lessons come with a summary, key points, key terms and takeaways for quick revision.
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Quizzes
85 questions across the course, with the right answer and why, the moment you pick.
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Assignments
1 subject assignments with clear tasks, so you practise what each subject actually asks of you.
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Notebook
Write notes beside the video, stamped with the exact time, and jump back to that moment later.
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Progress that follows you
Completed lessons, quiz scores and where you stopped, saved to your account on every device.
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Live chat
A members-only live chat beside every lesson: doubts, tips, stickers and GIFs with your batch. One ₹19 pass opens every live chat.
The Online MCA live chat. Study together, live.
A live chat right beside the video, shared by everyone studying Online MCA. Ask a Digital Image Processing doubt mid-lesson, share a shortcut, celebrate a finished semester.
- Live chat beside every lesson
- One live chat for all of Online MCA
- Members-only stickers & GIFs
- One ₹19 pass opens every live chat
Online MCA live chat
Anyone else finding Digital Image Processing tricky? 😅
Lesson 2 cleared it up for me. Watch it before the quiz
Doing it now, thanks!
Starting Android Programming tonight if anyone wants to go together
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Digital Image Processing: common questions
This subject covers how digital images are formed, enhanced in the spatial and frequency domains, restored, compressed, segmented, described and recognised. After it you will be able to choose and apply suitable filtering, restoration, compression, segmentation and morphological techniques to grayscale and colour images. It's the Digital Image Processing subject of Online MCA, Semester 3, in 17 lessons: Introduction to Digital Image Processing; Image Sampling and Quantization; Pixel Relationships: Neighbours, Connectivity and Distance; Gray Level Transformations; Histogram Equalization and Processing; Spatial Filters for Smoothing and Sharpening; Fourier Transform and Frequency Domain Filtering; Image Degradation Model and Noise Models; Estimating the Degradation Function and Inverse Filtering; Wiener and Constrained Least Squares Filtering; Image Compression: Redundancy and Models; Lossless and Lossy Compression and Standards; Edge Detection and Edge Linking; Thresholding and Region-Based Segmentation; Boundary and Regional Descriptors; Morphological Image Processing; Object Recognition and Pattern Classification.
Yes. Every video lesson is free to watch without an account. A free login unlocks study notes, quizzes, the assignment, a notebook and progress that syncs across devices.
About 4h 15m of video across 17 lessons, plus time for notes and quizzes. Go at your own pace; your place is saved.
Yes: “Digital Image Processing Problem Set: From Pixels to Recognition”. It comes after the last lesson, with a brief, clear tasks and how the work is judged.
A members-only live chat beside the video, shared by everyone studying Online MCA, where learners ask doubts, share tips and react with stickers and GIFs. One ₹19 pass, paid once from your InsideEdge wallet, opens the live chat for every degree.
No. It's a crash course to help you understand and revise the subject quickly. Use it alongside your official study material and assignments.