Image Processing

Learning Outcomes: 
After successful completion of this subject students will be able to:
• Understand image formation and the role human visual system plays in perception of gray and color image data.
• Get broad exposure to and understanding of various applications of image processing in industry, medicine, and defence.
• Learn the signal processing algorithms and techniques in image enhancement and image restoration.
• Acquire an appreciation for the image processing issues and techniques and be able to apply these techniques to real world problems.
• Be able to conduct independent study and analysis of image processing problems and techniques.
Syllabus: 
Unit NoTopics
1

Introduction and Digital Image Fundamentals:

Digital Image Fundamentals, Human visual system, Image as a 2D data, Image representation – Gray scale and Color images, image sampling and quantization

2

Image enhancement in Spatial domain:

Basic gray level Transformations, Histogram Processing Techniques,  Histogram equalization, Histogram Matching, Spatial Filtering, Low pass filtering, High pass filtering, Mexican Hat Transformation, 

3

Filtering in the Frequency Domain:

Introduction to the Fourier transform and frequency domain concepts, Extension to functions of two variables, low pass filter, high pass filter, Laplace transformation, Image Smoothing, Image Sharpening, Homo-morphic filtering

4

Image Restoration and Reconstruction:

Various noise models, image restoration using spatial domain filtering, image restoration using frequency domain filtering, Estimating the degradation function, Inverse filtering.

5

Color Image Processing:

Color Fundamentals, Color Models, Pseudo color image processing

6

Image Compression:

Fundamentals of redundancies, Basic Compression Methods: Huffman coding, Arithmetic coding, Error free compression, Lossy compression. LZW coding, JPEG Compression standard

7

Morphological Image Processing:

Erosion, dilation, opening, closing, Basic Morphological Algorithms : hole filling, connected components, thinning, skeletons

8

Image Segmentation:

point, line and edge detection, Thresholding, Regions Based segmentation, Edge linking and boundary detection, Hough transform

9

Object Recognition and Case studies:
Object Recognition- patterns and pattern classes, recognition based on decision – theoretic methods, structural methods, case studies – image analysis Application of Image processing in process industries

Text Books: 
Name : 
Digital Image Processing
Author: 
Rafael C Gonzalez
Richard E Woods
Publication: 
Pearson Education
Edition: 
3rd
Reference Books: 
Name: 
Image Processing, Analysis and Machine Vision‖
Author: 
Milan Sonka
Vaclav Hlavav
Roger Boyle
Publication: 
Thomson Learning
Edition: 
2nd
Name: 
Digital Image Processing
Author: 
Pratt W.K
Publication: 
John Wiley & Sons,2007
Name: 
Digital Image Processing Using Matlab
Author: 
Rafel C. Gonzalez
Richard E. Woods
Publication: 
Pearson Education
Name: 
Fundamentals of Digital Image Processing
Author: 
Anil K Jain
Publication: 
PHI
Syllabus PDF: 
AttachmentSize
PDF icon IMAGE PROCESSING SEM-6 CBA.BDA_.MA_.pdf222.81 KB
branch: 
CBA
BDA
MA
Course: 
2018
Stream: 
B.Tech