POORNIMA COLLEGE OF ENGINEERING, JAIPUR

DEPARTMENT OF COMPUTER ENGINEERING

Campus: Poornima College of Engineering Year/Section: IIIrd Date: 2 Jan 2025
Course: B.Tech. Semester/ Section –VI Cy, AI
Name of Faculty: Dr Rajesh Kumar Name of Subject : Digital Image Processing Code: 6CS3-01
Bathija
COURSE PLAN –BLOWN UP
SNo. TOPIC AS PER SYLLABUS BLOWN UP TOPICS ( up to 10 Times Syllabus)
1. Zero Lecture Introduction to the subject and its significance.
2. Unit -1 1.1 Digital Image Fundamental
1.2 Digital Image Processing
Digital Image representation, 1.3 Type of DIP
Sampling & Quantization, Steps in 1.4 Purpose of Image Processing
image Processing, Image 1.5 Fundamental Steps in DIP
acquisition, color image 1.5.1 Image Acquistion
representation.. 1.5.2 Image Enhancement
1.5.3 Image Segmentation
1.5.4 Feature Extraction and Object Discription
1.5.5 Pattern recognition
1.6 Steps of Image Processing
1.6.1 Image Enhancement
1.6.2 Image Restoration
1.6.3 Image Compression
1.6.4 Image Analysis
1.6.5 Image Synthesis
1.7 Sampling and Quantization
1.7.1 Sampling
1.7.2 Quantization
1.8 Image Formation in an Eye
1.9 Colour Image Processing
1.9.1 Full Color Processing
1.9.2 Pseudo Color Processing
1.10 Color Fundamental
1.11 Resampling
1.11.1 UpSampling
1.11.2 Down Sampling
1.12 Cone Structure
1.13 Types of Image
1.13.1 Binary Image
1.13.2 8 Bit Colour Format
1.13.3 16 Bit Colour Format
1.13.4 24 Bit Colour Format
1.13.5 CMYK Model
1.14 Conversion of Image
1.14.1 RGB to Hex
1.14.2 RGB to Grey
1.15 Zooming Method
1.15.1 Pixel Specification
1.15.2 Zero Overhold
1.15.3 K – Times Zooming
1.16 Distances Measurement
1.17 Neighbourhood of Pixels
1.17.1 N4 Concept
1.17.2 N8 Concept
1.17.3 Nm Concept
1.17.4 Conversion of gray to binary
3. Unit -2 2.1 Basic Intensity Transformation Function
2.1.1 Linear Function
Intensity transform functions, 2.1.1.1 Image Negative
histogram processing, Spatial 2.1.1.2 Log Transformation
filtering, Fourier transforms and its 2.1.1.3 Power Law Function
properties, frequency domain filters, 2.1.2 Non Linear Function
colour models, Pseudo colouring, 2.1.2.1 Square Function
colour transforms, Basics of Wavelet 2.1.2.2 Square Root Function
Transforms. 2.1.2.3 Logarithmic Function
2.1.2.4 Exponential Function
2.2 Piecewise Linear Function
2.2.1 Contrast Stretching and its Variant
2.2.2 Intensity Scaling
2.2.3 Bit Plane Slicing
2.3 Histogram Based Technique
2.3.1 Histogram Equalization
2.3.2 Histogram Stretching
2.3.3 Histogram Sliding
2.3.4 Histogram Specification
2.4 Spatial Filtering
2.4.1 Image Smoothing
2.4.2 Image Sharpening
2.4.3 Histogram Stretching
2.5 Smoothing Spatial Filter
2.5.1 Image Smoothing
2.5.2 Image Sharpening
2.6 Smoothing Spatial Filter
2.6.1 Linear Filter
2.6.1.1 Mean Box Filter
2.6.1.2 Weighted Average Filter
2.6.1.3 Gaussian Filter
2.6.2 Non Linear Filter
2.6.2.1 Median Filter
2.6.2.2 Max Filter
2.6.2.3 Min Filter
2.7 Sharpening Spatial Filter
2.7.1 Blurring
2.7.2 Sharpning
2.8 Correlation and Convolution in Spatial Filtering
2.8.1 Cross Correlation
2.8.2 Convolution
2.9 Fourier Transform
2.9.1 Discrete Fourier Transform
2.9.2 Inverse Fourier Transform
2.9.3 Properties of Fourier Transform
2.10 Frequency Domain Filter
2.11 Wavelet Series Expansion
2.12 Pseudo Colour Image Processing
2.13 Colour Model
2.13.1 RGB Colour Model
2.13.2 CMK and CMYK Model
2.13.3 HSI Model
4. Unit-3 3.1 Image Degradation and Restoration Process
3.2 Noise Models
Image degradation and restoration 3.2.1 Sequential Frequency Properties of Noise
process, Noise Models, Noise Filters, 3.2.2 Noise Probability Density Functions
degradation function, Inverse 3.2.2.1 Rayleigh Noise
Filtering, Homomorphism Filtering. 3.2.2.2 Gaussian Noise
3.2.2.3 Erlang Noise
3.2.2.4 Exponential Noise
3.2.2.5 Uniform Noise
3.2.2.6 Impulse ( Salt and Pepper Noise)
3.3 Periodic Noise
3.4 Estimation of Noise Parameter
3.5 Noise filter
3.5.1 Mean Filter
 3.5.2 Order Statistics Filter
3.5.3 Adaptive Filter
3.6 Degradation Function
3.6.1 Estimation by Image Observation
3.6.2 Estimation by Experimentation
3.6.3 Estimation by Modelling
3.7 Inverse Filtering
3.8 Hormorphic Filtering

4. Unit-4 4.1 Image Compression Fundamentals

4.2 Coding Redundancy
Coding redundancy, Inter-pixel 4.3 Interpixel Redundancy
redundancy, Psycho-visual 4.4 Psychovisual Redundancy
redundancy, Huffman Coding, 4.5 Fundamental Coding Theorem
Arithmetic coding, 4.5.1 Huffman Coding
Lossy compression techniques, JPEG 4.5.2 Airthmetic Coding
Compression. 4.6 Lossy Compression Technique
4.6.1 Lossy Predictive Coding
4.6.1.1 Optimal Predictor
4.6.1.2 Optimal Quantization
4.6.2 Transform Coding
4.6.2.1 Transform Selection
4.6.2.2 Subimage Size Selection
4.6.2.3 Bit Allocation
4.6.3 Wavelet Coding
4.6.3.1 Wavelet Selection
4.6.3.2 Decomposition Level Selection
4.7 JPEG Compression
4.7.1 JPEG 2000

6. Unit-5 5.1 Image Segmentation

5.2 Detection of Discontinuities
Point, Line and Edge Detection, 5.2.1 Point Detection
Thresholding, Edge and Boundary 5.2.2 Line Detection
linking, Hough transforms, Region 5.2.3 Edge Detection
Based Segmentation, Boundary 5.2.3.1 Basic Formula
representation, Boundary Descriptors. 5.2.3.2 Gradient Operator
5.2.3.3 The Laplacian
5.3 Edge Linking and Boundary Detection
5.3.1 Local Processing
5.4 Hough Transform
5.4.1 Global Processing through Hough Transform
5.4 Region Based Segmentation
5.4.1 Basic Formulation
5.4.2 Region Growing
5.4.3 Region Splitting and Merging
5.5 Boundary Representation
5.5.1 Chain Codes
5.5.2 Polygonal Approximation
5.5.2.1 Minimal Perimeter Polygons
5.5.2.2 Merging Techniques
5.5.2.3 Splitting Technique
5.5.3 Signatures
5.5.4 Boundary Segments
5.5.5 Skeletons
5.6 Boundary Description
5.6.1 Some Simple Descriptors
5.6.2 Shape Numbers
5.6.3 Fourier Descriptor
5.6.4 Statistical Moments