MCA-241
Digital Image Processing
Processing pictures as numbers — intensity transforms and spatial filtering, frequency-domain enhancement and restoration, compression, and segmentation.
- Semester
- Semester 3
- Credits
- 3
- Scheme
- 3 lectures a week
- Contact hours
- 46 across four units
- Category
- Core Elective - III
What the paper is
Digital Image Processing treats an image as a two-dimensional signal and applies signal processing to it. The paper is organised by domain: Unit I works in the spatial domain directly on pixels, Unit II moves to the frequency domain via the Fourier transform, and the last two units apply both to compression and segmentation.
The spatial-versus-frequency distinction is the thing to get right early, because the same operation appears in both. A smoothing filter is a convolution in one and a multiplication in the other, and knowing why is most of the conceptual content of Unit II.
Units I to III carry twelve hours each, which is unusually even, and the paper is examined numerically throughout: histogram equalisation, filter masks applied to a pixel neighbourhood, Huffman codes for grey levels, thresholds computed from a histogram.
What to concentrate on
- Histogram equalisation on a small image with eight grey levels is the single most predictable question in the paper. Work it end to end until the table is automatic.
- Be able to apply a 3×3 mask — averaging, weighted averaging, Laplacian, Sobel — to a given neighbourhood and say what it does to edges and to noise. Spatial filtering questions are all variations on that.
- For segmentation, concentrate on edge detection operators and on thresholding, including how a threshold is chosen from a bimodal histogram. Region growing and splitting-and-merging are the descriptive complement.
Syllabus, unit by unit
Summarised from the approved scheme. Hours are the contact hours the scheme allots, which is a fair guide to where the marks sit.
Unit I · Digital images and spatial transformations
12 hours- Image processing steps and digital image fundamentals
- Image sampling and digitisation, relationships between pixels, mathematical tools for image processing
- Intensity transforms and histogram processing
- Spatial filtering: smoothing and sharpening spatial filters
Unit II · Frequency domain enhancement and restoration
12 hours- Basics of signals and systems, the Fourier transform and the frequency domain
- Filtering images in the frequency domain: smoothing and sharpening filters, homomorphic filtering
- Image restoration: inverse filtering, the algebraic approach
- Wiener (LMS) restoration, constrained least squares restoration, interactive and other methods
Unit III · Reconstruction and compression
12 hours- Image reconstruction from projections: the filtered back-projection algorithm, algebraic reconstruction
- Image data compression fundamentals
- Image compression coding, models and standards
- Basic image compression methods
Unit IV · Segmentation and morphology
10 hours- Segmentation fundamentals and the detection of discontinuities
- Edge linking and boundary detection
- Thresholding and region-based segmentation
- Morphological processing
How it is examined
- Question 1 is compulsory, covers the whole syllabus, and is made of objective or short-answer questions. It carries 12 marks.
- The rest of the paper is four units matching the four above. Each unit sets two questions and you attempt one, so there are four more answers at 12 marks each.
- Examiners are asked to map every question to a course outcome using Bloom's taxonomy, which is why questions are phrased as “apply”, “analyse” or “evaluate” rather than only “explain”.
The pass mark is 40 out of 100 in every paper.
Learning objectives
- Fundamental steps in digital image processing and image formation
- Techniques to improve the visual appeal of an image
- Filtering in the spatial and frequency domains for enhancement
- The image restoration process
- Image compression and segmentation, and implementing algorithms for image analysis
Assumed beforehand
- Basics of digital signal processing
- Transform techniques
Paired in the scheme with MCA-267 Lab based on Core Elective III.
Books it is taught from
- TB1R. C. Gonzalez and R. E. Woods, “Digital Image Processing”, Pearson Education, 4th edition, 2018
- TB2Milan Sonka, Vaclav Hlavac and Roger Boyle, “Image Processing, Analysis and Machine Vision”, Cengage Learning, 4th edition, 2014
Notes for MCA-241
Lecture notes, question papers and assignments filed against MCA-241 are in the library. Everything there has been checked by a moderator before it appears, and an account is free — new ones are approved by hand, which is what keeps the shelf clean.