| Course Code |
CSC730 |
| Course Title |
Advanced Image Processing |
| Credit Hours |
3 |
| Prerequisites by Course(s) and Topics |
Students should have a solid mathematical foundation and be familiar with basic programming concepts, probability and random variables, linear algebra & vectors and matrices, basic concepts of image processing. |
| Assessment Instruments with Weights (homework, quizzes, midterms, final, programming assignments, lab work, etc.) |
SESSIONAL (Quizzes, Assignments, Presentations) =25 %
Midterm Exam =25 %
Final Exam = 50%
|
| Course Coordinator |
Dr. Muhammad Asif |
| URL (if any) |
Resources Google Classroom, MSCS (siixwr3) |
| Current Catalog Description |
Introduction: What is Digital Image Processing (DIP)?, Origins of DIP, Applications of DIP, Fundamental steps in DIP, Components of DIP system Digital Image Fundamentals: Elements of visual perception, Light and electromagnetic spectrum, Image sampling and quantization, Image Sensors, Digital Camera technologies, Basic relationships between pixels, Adjacency, Connectivity, Path, Region, Distance Measures and Linear and Non-linear operations. Arithmetic and Logical Operations on Images: Mathematical/Logical operations on images (multiplication, division, NOT, OR, AND, XOR, XNOR) and their application Image Enhancement in the Spatial Domain: Types of image enhancement operations, Basic gray level transformation, Enhancement using arithmetic/logical operations, Contrast stretching, Log transformation, power law transformation, Gamma correction, Piecewise-linear transformation, Contrast stretching and Bit Plan slicing, Histogram, Applications of histogram, histogram equalization, Local enhancement using statistical parameters from histogram. Spatial Domain Operations: Types of spatial domain operations, Basics of spatial filtering, Linear vs no-linear filtering, Correlation and convolution, Low pass and high pass filtering, smoothing and sharpening spatial filter, Laplacian and Gradient Operators. Geometrical Transformation and Image Registration: Geometrical Transformations: Why GT?, basic components of GT, Translation, rotation, scaling, shear and affine transformation, Mapping Schemes, Interpolation, Image Registration, Applications of image registration. Image Restoration: Degradation model for digital Image, Noise and its models, Effect of noise on images and histogram, Noise parameters estimation, Noise reduction techniques. Morphological Image Processing: Dilation and Erosion, Opening and Closing, Hit or miss transformation, Basic morphological algorithms Frequency Domain Operations: Fourier series and Fourier transform, Application of Fourier transform, Discrete Fourier Transform, Properties of Fourier Transform , Image enhancement in frequency domain, Frequency domain filtering, Low pass, band pass and high pass filtering. Color Image Processing: Color Fundamentals, Color models, Pseudo color image processing, basics of color image processing, color transformation, smoothing and sharpening of color images Image Compression: Basics of Image compression, Image compression models, Error free compression, Lossy Compression, JPEG compression standards (JPEG and JPEG2000) |
| Textbook (or Laboratory Manual for Laboratory Courses) |
• R. C. Gonzalez and R. E. Woods, “Digital Image Processing”, 4th edition, Pearson Education, Inc., 2008. • R. C. Gonzalez, R. E. Woods and S.L. Eddins “Digital Image Processing using MATLAB”, 3rd Edition, Pearson Education, Inc., 2004. |
| Reference Material |
Practical Image And Video Processing Using MATLAB by OGE MARQUES |
| Course Goals |
At the end of this course students will be able: To understand the basic building blocks of digital image processing systems. To develop digital image processing algorithm using MATLAB and Python. To apply the digital image processing techniques to solve the real-life problems To develop digital image processing based applications and systems To critical analyze the digital image processing applications and systems. |
| Course Learning Outcomes (CLOs): |
| At the end of the course the students will be able to: | Domain | BT Level* |
|---|
| 1. To understand the basic building blocks of digital image processing systems. |
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| 2. To develop digital image processing algorithm using MATLAB and Python. |
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| 3. To apply the digital image processing techniques to solve the real-life problems |
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| 4. To develop digital image processing based applications and systems |
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| 5. To critical analyze the digital image processing applications and systems. |
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| * BT= Bloom’s Taxonomy, C=Cognitive domain, P=Psychomotor domain, A= Affective domain |
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| Topics Covered in the Course, with Number of Lectures on Each Topic (assume 15-week instruction and one-hour lectures) |
| Week | Lecture | Topics Covered |
|---|
| Week 1 |
1 |
What is Digital Image Processing (DIP)? Origins of DIP, Applications of DIP. |
|
2 |
Fundamental steps in DIP, Components of DIP system |
| Week 2 |
3 |
Elements of visual perception, Light and electromagnetic spectrum, Image sampling and quantization |
|
4 |
Image Sensors, Digital Camera technologies |
| Week 3 |
5 |
Basic relationships, between pixels, Neighbors of pixel, Adjacency, Connectivity |
|
6 |
Distance Measures, Euclidean, City Block, Chess-Board distance, Path, Region and Boundaries |
| Week 4 |
7 |
Arithmetic and Logical Operations on Images: Addition, Subtraction, Multiplication and Division operations and their applications. Logical operations on images (OR, AND, NOT, NAND, NOR, XOR, XNOR) and their application |
|
8 |
Image Enhancement: Types of image enhancement operations, Basics concepts, Linear and Non-linear operations. Basic gray level transformation |
| Week 5 |
9 |
Image enhancement using arithmetic/logical operations, Contrast stretching, Log transformation, power law transformation, Gamma correction, Piecewise-linear transformation, Bit Plan slicing |
|
10 |
Histogram, Applications of Histogram and Interpreting Image Histograms |
| Week 6 |
11 |
Histogram equalization and Matching |
|
12 |
Histogram Sliding, stretching and shrinking, Local histogram processing, Local enhancement using statistical parameters from histogram. |
| Week 7 |
13 |
Spatial domain operations, Types of spatial domain operations, Basics of spatial filtering, Linear vs no-linear filtering, Correlation and convolution |
|
14 |
Low pass and high pass filtering, smoothing and sharpening spatial filter, Order statistics filters |
| Week 8 |
1 hours |
Mid Term |
| Week 9 |
15 |
Spatial filtering for image sharpening, 1st order and 2nd order derivatives Laplacian operators for image enhancements |
|
16 |
Sharpening filters: Unsharp masking, High Boost filtering, Gradient operators |
| Week 10 |
17 |
Image restoration, Degradation model for digital Image, Noise and its models. |
|
18 |
Effect of noise on images and histogram , Noise parameters estimation, Noise reduction techniques: Mean Filters (Arithmetic mean, Geometric mean, Harmonic mean, Contraharmonic mean ) |
| Week 11 |
19 |
Noise reduction techniques: Order-statistics filters (Median filter, Max and Min filters, Midpoint filter, Alpha-trimmed mean filters) |
|
20 |
Noise reduction techniques: Adaptive filters (Adaptive local noise reduction filter and Adaptive median filter) |
| Week 12 |
21 |
Image registration and its Applications, Control point image registration |
|
22 |
Geometric Spatial Transformation: Translation, Rotation, Scaling and Affine transformation, Mapping schemes, Interpolation |
| Week 13 |
23 |
Morphological Image Processing, Structuring element, Dilation and Erosion |
|
24 |
Compound operations (Opening and Closing), Basic morphological algorithms |
| Week 14 |
25 |
Fourier series and Fourier transform, Application of Fourier transform, Discrete Fourier Transform, Properties of Fourier Transform, Image enhancement in frequency domain |
|
26 |
Frequency domain filtering, Low pass, band pass and high pass filtering |
| Week 15 |
27 |
Color Fundamentals, Color models, Pseudo color image processing, basics of color image processing |
|
28 |
Color transformation, smoothing and sharpening of color images |
| Week 16 |
29 |
Basics of Image compression, Image compression models, Error free compression, Lossy Compression |
|
30 |
JPEG compression standards (JPEG and JPEG2000) |
| Week 17 |
2 hours |
Final Term |
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| Laboratory Projects/Experiments Done in the Course |
Yes |
| Programming Assignments Done in the Course |
Yes |
