Course

Unit I

Digital Image Fundamentals: Elements of Visual Perception- Simple Image Formation Model -Image Sensing and Acquisition-Image Sampling and Quantization – Basic Relationships between Pixels – Image interpolation.

Unit II

Intensity Transformations and Filtering: Basic Intensity transformation Functions – Histogram Processing – Fundamentals of Spatial Filtering –Smoothing and Sharpening Spatial Filters. Filtering in Frequency Domain: 2D Discrete Fourier Transforms – Basics of filtering – Image Smoothing and Image Sharpening Using Frequency Domain Filters – Selective Filtering.

Unit III

Image Restoration: Noise Models – Restoration using Spatial Filters – Periodic Noise Reduction by Frequency Domain Filters.

Unit IV

Morphological Image Processing: Erosion – Dilation – Opening – Closing – Hit-or-Miss Transform – Extraction of Connected Components.

Unit V

Image Segmentation: Fundamentals – Point, Line and Edge Detection – Thresholding-Region Based Segmentation – Region Growing – Region Splitting and Merging.

Introduction to Color image processing.

Image Compression – need for image compression, Huffman, Run-length encoding, shift codes, Vector quantization, Transform coding, JPEG standard, MPEG.

The lab experiments/ Case studies shall be done using MATLAB/ Python.

Course Description  

Image processing deals with methods to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. This course provides basic knowledge about digital images, Imaging geometry, Image transforms, Image enhancement and filtering, Image restoration, Image segmentation, and morphological operations which are useful in any computer vision applications. Image Compression – need for image compression, Huffman, run-length encoding, shift codes, Vector quantization, Transform coding, JPEG standard, MPEG. 

Course Objectives 

• To introduce students to the basics of digital image processing applicable to binary, gray scale and colour images. 

• To familiarize students to various algorithms in spatial and frequency domain relevant to image enhancement and restoration. 
• To provide an opportunity to learn image compression and segmentation and its applications.  

Course Outcomes 

CO-PO Mapping 

• Rafael C. Gonzalez and Richard E. Woods,” Digital Image Processing”, 4th Edition, Pearson, 2018.
• A K. Jain, Fundamentals of digital image processing, Prentice Hall of India, 1989.
• Al Bovik, The Essential Guide to Image Processing, Academic Press, 2009.
• Milan Sonka, Vaclav Hlavac and Roger Boyle, Image Processing, Analysis, and Machine Vision, Thomson Learning, 2008.
• S Jayaraman, S Esakkirajan and T Veerakumar, Digital Image Processing, McGraw Hill Education, 2009.
• Arthur R. Weeks, Jr., “Fundamentals of Electronic Image Processing”, First Edition, PHI,1996.