ECE 09552 RU Degradation Models and Digital Image Restoration Essay The objective of this project is to study the effects of image degradations (in particu

ECE 09552 RU Degradation Models and Digital Image Restoration Essay The objective of this project is to study the effects of image degradations (in particular, blurring) and implement appropriate restoration techniques. This project has three parts. In Part 1, you will implement degradation and restoration techniques on a simulated image. In Part 2, you will attempt to replicate the degradation and restoration models illustrated in the textbook. In Part 3, you will attempt to restore a degraded image, given some information about the nature of the degradation and/or the original image characteristics.The link to this project is: http://users.rowan.edu/~shreek/spring20/dip/labs/lab3.htmlI want the above lab project to be coded in matlab. S. Mandayam/ DIP/ECE Dept./Rowan University
Digital Image Processing
ECE.09.452/ECE.09.552
Restoration
Dr. Shreekanth Mandayam
Electrical & Computer Engineering
Rowan University
S. Mandayam/ DIP/ECE Dept./Rowan University
Plan
• Digital Image Restoration
• Environmental Models
• Image Degradation Model
• Image Restoration Model
• Point Spread Function (PSF) Models
• Linear Algebraic Restoration
• Unconstrained (Inverse Filter, Pseudoinverse Filter)
• Constrained (Wiener Filter, Kalman Filter)
• Lab 3
S. Mandayam/ DIP/ECE Dept./Rowan University
DIP: Details
Digital Image Processing
Digital Image Characteristics
Spatial
Gray-level
Spectral
Histogram
DFT
DCT
Pre-Processing
Enhancement
Point Processing
Masking
Restoration
Filtering
Degradation Models
Inverse Filtering
Compression
Information Theory
Lossless
Lossy
LZW (gif)
Transform-based (jpeg)
Segmentation
Edge Detection
Description
Shape Descriptors
Texture
Morphology
Wiener Filtering
S. Mandayam/ DIP/ECE Dept./Rowan University
Image Preprocessing
Restoration
Enhancement
Spectral
Domain
Spatial
Domain
Point Processing
• >>imadjust
• >>histeq
Spatial filtering
• >>filter2
Filtering
• >>fft2/ifft2
• >>fftshift
• Inverse filtering
• Wiener filtering
S. Mandayam/ DIP/ECE Dept./Rowan University
Enhancement vs. Restoration
• “Better” visual
representation
• Remove effects of
sensing environment
• Subjective
• Objective
• No quantitative
measures
• Mathematical, model
dependent quantitative
measures
S. Mandayam/ DIP/ECE Dept./Rowan University
Degradation Model
f(x,y)
h(x,y)
S
g(x,y)
n(x,y)
Degradation Model: g = h*f + n
demos/readme.txt
demos/circ.m
demos/psf.m
demos/blur.m
S. Mandayam/ DIP/ECE Dept./Rowan University
Restoration Model
f(x,y)
Degradation
Model
Restoration
Filter
Unconstrained
demos/readme.txt
demos/circ.m
demos/psf.m
demos/blur.m
demos/restore.m
• Inverse Filter
• Pseudo-inverse Filter
f(x,y)
Constrained
• Wiener Filter
S. Mandayam/ DIP/ECE Dept./Rowan University
f(x,y)
Build
degradation model
Analyze using
algebraic techniques
Formulate
restoration algorithms
Implement using
Fourier transforms
f(x,y)
Approach
g = h*f + n
demos/degrade.m
g = Hf + n
W -1 g = DW -1 f + W -1 n
f = H -1 g
F(u,v) = G(u,v)/H(u,v)
S. Mandayam/ DIP/ECE Dept./Rowan University
Degradation & Restoration Examples: Gonzalez & Woods
Atmospheric Turbulence Model
S. Mandayam/ DIP/ECE Dept./Rowan University
Degradation & Restoration Examples: Gonzalez & Woods
Example 5.9: Inverse Filtering
S. Mandayam/ DIP/ECE Dept./Rowan University
Degradation & Restoration Examples: Gonzalez & Woods
Example 5.10: Wiener Filtering
S. Mandayam/ DIP/ECE Dept./Rowan University
Degradation & Restoration Examples: Gonzalez & Woods
Example 5.11: Inverse and Wiener Filtering
S. Mandayam/ DIP/ECE Dept./Rowan University
Lab 3: Degradation Models
and Digital Image
Restoration
http://users.rowan.edu/~shreek/spring20/dip/labs/lab3.html
S. Mandayam/ DIP/ECE Dept./Rowan University
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