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Core/Elective: Elective Semester: 3 Credits: 4
Course Description

The aim of this course is to inculcate a comprehensive knowledge about various Digital Image and Video Processing techniques.

Course Objectives

Give an in-depth knowledge about the basic theory and algorithms related to Digital Image and Video Processing.
Provide awareness about the current technologies and issues specific to Digital Image and Video Processing.
Provide hands-on experience in using computers to process digital images and Videos.
Expose students to Python and OpenCV library to do image and video processing tasks.

Course Content

Module I
Signals: Impulse Sequence - Exponential Sequence - Periodic Sequence. Linear Systems - ShiftInvariant systems - Linear Shift Invariant (LSI) systems – Convolution - Correlation. Image Transforms: Fourier Transform - Discrete Fourier Transform - Z-transform – KL Transform. Causal Systems - Random Signals - Stationary Process - Markov Process.

Module II
Intensity Transformation and Spatial Filtering: Intensity Transformation Functions. Histogram Processing: Histogram Equalization - Histogram Matching. Image enhancement: Arithmetic/Logic operations - Image Subtraction - Image Averaging. Spatial Filtering: Smoothening Spatial Filters -Sharpening Spatial Filters - Laplacian Filter - Unsharp masking - High Boost Filter. Gradient operators: Edge detection filters. Frequency Domain Smoothening - Frequency Domain Sharpening Filters - Laplacian in Frequency domain - Homomorphic Filtering.

Module III
Image degradation/Restoration process model - Noise probability density functions - Spatial Filtering: Mean Filters - Order-statistics filter - Adaptive Filters - Periodic Noise Reduction –Bandreject filters - Band-pass filters - Notch filters. Inverse filtering - Wiener filtering - Performance measures. Color image processing: Color fundamentals - Color models – RGB, CMYK – HIS -Color image smoothening and sharpening – Color image histogram - Color edge detection.

Module IV
Point and line detection - Hough Transform. Image Segmentation: Fundamentals – Thresholding – Otsu’s optimum global thresholding - Region-based segmentation: Region growing - Region Splitting and Merging - Segmentation using Morphological Watersheds.

Module V
Color video processing: Video display - Composite versus component video - Progressive and interlaced scan. Motion estimation: Optical flow - pixel based motion estimation - block matching algorithm - deformable block matching algorithm - Global and region based motion estimation -multi-resolution motion estimation - Feature based motion estimation. Stereo and multi-view sequence processing: Depth perception - Stereo imaging principle - Disparity estimation.


1. Rafael C. Gonzalez, Richard E. Woods, "Digital Image Processing", 4 th Ed., Pearson, March 2017.
2. Anil K. Jain, “Fundamentals of Digital Image Processing”, Pearson, 1 st Ed., 1988.
3. William K. Pratt, “Digital Image Processing: PIKS Scientific Inside”, John Wiley & Sons, 4 th Ed., 2007.
4. Azriel Rosenfield, Avinash C. Kak, "Digital Picture Processing", Morgan Kaufmann, 2 nd Ed., 1982.
5. Bernd Jahne, “Digital Image Processing”, Springer, 6 th Ed., 2005.
6. Yao Wang, Jorn Ostermann, Ya-Qin Zhang, "Video Processing and Communications", Pearson, 1st Ed., 2001.
7. Alan C. Bovik, "The Essential Guide to Video Processing", Academic PRess, 2nd Ed., 2009 8. A. Murat Tekalp, "Digital Video Processing", Prentice Hall, 2nd Ed., 2015.

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