MCA-20-44 (iii): Digital Image Processing
Type: Elective
Contact Hours: 4 hours/week
Examination Duration: 3 Hours
Mode: Lecture
External Maximum Marks: 75
External Pass Marks: 30(i.e. 40%)
Internal Maximum Marks: 25
Total Maximum Marks: 100
Total Pass Marks: 40(i.e. 40%)
Instructions to paper setter for End semester examination:
Total number of questions shall be nine. Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.
Course Objectives: Provide an introduction to the basic concepts and methodologies for digital image processing. To develop a foundation that can be used as a basis for further studies and research. Introduce the students to the fundamental techniques and algorithms used for acquiring, processing and extracting useful information from digital images.
Course Outcomes: At the end of this course, the student will be able to:
MCA-20-44(iii).1 get acquainted with digital image fundamentals and its applications and get acquainted with the image representation and description methods;
MCA-20-44(iii).2 Learn and perform image pre-processing and enhancement to improve the image for further processing;
MCA-20-44(iii).3 reconstruct photometric properties degraded by the imaging process and partition a digital image into multiple segments;
MCA-20-44(iii).4 represent and analyse images at different resolutions , process images according to their shapes, and apply compression techniques to reduce the storage space of images.
Unit – I
Digital Image Fundamentals: Introduction to Digital Image Processing and its applications; Components of an Image Processing System.
Image Representation and Description: Image Representation ; Digital Image Properties; Boundary descriptors; Regional descriptors; Steps in Digital Image Processing; Elements of Visual perception; Image Sensing and Acquisition; Image Sampling and Quantization; Relationship between Pixels; Color Representation.
Data Structures for Image Analysis: Levels of Image Data Representation; Traditional Image Data Structures: Matrices, Chains, Topological Data Structures, Relational Structures; Hierarchical Data Structures: Pyramids, Quadtrees, Other Pyramidal Structures.
Unit – II
Image Pre-Processing: Pixel Brightness Transformations: Position-Dependent Brightness Correction, Gray-Scale Transformation; Geometric Transformations: Pixel Co-ordinate Transformations, Brightness Interpolation; Local Pre-Processing.
Image Enhancement: Spatial Domain: Gray level transformations; Histogram processing; enhancement using arithmetic and logic operators; Basics of Spatial Filtering; Smoothing and Sharpening Spatial Filtering.
Frequency Domain: Introduction to Fourier Transform; Filtering in the Frequency Domain; Smoothing and Sharpening frequency domain filters; Homomorphic Filtering.
Unit – III
Image Restoration and Segmentation: Noise models; Mean Filters; Order Statistics; Adaptive filters; Noise Reduction by Frequency Domain Filtering; Inverse and Wiener filtering; Constrained Least Squares Filtering.
Segmentation: Point, line, and Edge Detection; Edge Linking and Boundary detection; Thresholding; Region based segmentation; Edge based Segmentation; Segmentation by Morphological Watersheds; Matching.
Color Image Processing: Color Fundamentals, Color Models, Pseudocolor Image Processing.
Unit – IV
Wavelets and Multiresolution Processing: Background: Image Pyramids; Subband coding; Multiresolution expansions.
Morphological Image Processing: Preliminaries, Erosion and Dilation, Opening and Closing, The Hit-or-Miss Transforms, Some Basic Morphological Algorithms.
Compression – Fundamentals ; Image Compression models; Error-Free Compression; Variable Length Coding, LZW coding, Bit-Plane Coding, Lossless Predictive Coding; Lossy Compression: Lossy Predictive Coding, Transform Coding, wavelet Coding; Image Compression Standards.
Text Books:
⦁ Rafael C. Gonzales, Richard E. Woods, Digital Image Processing, Pearson Education.
Reference Books:
⦁ Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, Digital Image Processing Using MATLAB, Third Edition ,Tata McGraw Hill .
⦁ Anil Jain K., Fundamentals of Digital Image Processing, PHI Learning.
⦁ Willliam K Pratt, Digital Image Processing, John Willey.
⦁ Malay K. Pakhira, Digital Image Processing and Pattern Recognition, First Edition, PHI Learning.
⦁ S. Jayaraman, S. Esakkirajan and T. Veerakumar, Digital Image Processing, McGraw Hill
⦁ B. Chanda ,D.DuttaMajumder, Digital Image Processing and Analysis, Prentice Hall of India.