EE440 Introduction to Digital Imaging Systems
Lab. 7
(50%) 7_1.bmp is a blurred image, emulating a movement of camera during the shooting
of the
LENNA image. The mathematical expression for the blurring is
i.e., the average of ten consecutively shifted im
5. Image Enhancement in the
Frequency Domain
Chapter 4 (Image Enhancement in the
Frequency Domain)
1
Fourier Series
2
Fourier Transform
Fourier Transform of a real function is generally complex
F ( f ( x ) F ( u )
f ( x ) exp[ j 2 ux ] dx R ( u ) jI ( u
5. Image Enhancement in the
Frequency Domain
Chapter 4 (Image Enhancement in the
Frequency Domain)
1
Fourier Series
2
Fourier Transform
Fourier Transform of a real function is generally complex
F ( f ( x ) F (u )
f ( x ) exp[ j 2ux]dx R (u ) jI (u ) F (
8. IMAGE TRANSFORMS
2-D Discrete Cosine Transform
(Section 8.2.8, pp.566-579)
Discrete Fourier Transform (DFT)
1-D DFT pair for a signal sequence x(n) of length N:
1
X (k )
N
x(n )
N 1
k0
N 1
n0
x(n ) e
X (k ) e
j 2 nk
N
j 2 nk
N
,
,
k 0 ,., N 1
n 0
EE 440 Introduction to Digital Imaging Systems
Course Info
Credits: 4
Goal: The goal of this course is to introduce to students the basic theory of image
processing and many key aspects in image computing and digital video systems, including
various stand
EE 440 Final Project Autumn 2015
November 8, 2015
Project Due: Friday 12/11/2015 11:59pm
1
Description
The purposes of this project is to help you integrate what you have learned in this class and
stimulate your interest in digital image processing throug
EE440 - Digital Image Processing, Fall 2015
Homework Assignment 5
Due: 3:30 pm Wednesday 11/11/2015, before the lecture begins.
Note: Please follow the homework submission guidelines on the class webpage.
Solve the following problems using your own Matlab
6. Edge Detection and
Object Segmentation
Chapter 10 (Image Segmentation)
1
Edge Detection Using Derivative Operators
Edges: the image portions which have large gradients
Magnitude of the gradient
f ( n 1, n 2 )
| f ( n1 , n 2 )|
n1
often approximat
11. Video Compression
Chapter 8 (8.1, 8.2)
Digital Video Compression
Applications: video telephony,
broadcast, streaming, Blu-ray disc,
storage,
Raw video bitrate: 9.1Mbps for
176144@30fps; 1.5Gbps for
19201080@60fps
Represent the source data with the
h
8. IMAGE RESTORATION
Chapter 5 (Image Restoration, 5.1-5.3, 5.5-5.8)
1
Image Restoration
A process that attempts to reconstruct or recover an
image that has been degraded by using some a priori
knowledge of the degradation phenomenon.
2
Degradation Mode
9. IMAGE TRANSFORMS
2-D Discrete Cosine Transform
(Section 8.2.8, pp.566-579)
Discrete Fourier Transform (DFT)
1-D DFT pair for a signal sequence x(n) of length N:
1
X (k )
N
x(n )
N 1
k0
N 1
n0
x(n ) e
X (k ) e
j 2 nk
N
j 2 nk
N
,
,
k 0 ,., N 1
n 0
7. Mathematical Morphology
Chapter 8 pp.627-664 (Morphology)
1
Mathematical Morphology
J. Serra, Image Analysis and Mathematical Morphology,
Academic Press, London, 1982.
A range of non-linear image processing techniques that deal with
the shape or morph
EE440 - Introduction to Digital Imaging Systems, Autumn 2015
Assignment 1
Due: 3:30 pm Wednesday 10/14/2015, before the lecture begins.
Note: Please follow the homework submission guidelines on the class webpage.
1) Image processing and commercial tools (
EE440 - Introduction to Digital Imaging Systems, Fall 2015
Homework Assignment 2
Due Time/Date: 3:30 pm, Wednesday, 10/14/2015, before the class
Please follow the homework submission guidelines.
For problem 3-4, solve the problems using your own Matlab pr
EE440 - Introduction to Digital Imaging Systems, Fall 2015
Homework Assignment 3
Due Time/Date: 3:30 pm, Wednesday, 10/21/2015
Please follow the submission guidelines.
1. The color of 3_1.bmp does not look right. Please write your own program to make the
HW1
Image Enhancement and Manipulation
Prob. 1 (25 points) Quantization.
a) A typical digital image has pixel values between 0 and 255 represented in 8-bit numbers.
Now, suppose you are allowed to have only 5 bits to represent each pixel value, which
can
7. Image Transform
2-D Discrete Cosine Transform (Section 8.5.2)
Discrete Fourier Transform (DFT)
1-D DFT pair for a signal sequence x(n) of length N:
1
X (k )
N
x(n )
N 1
n0
x(n ) e
N 1
X (k ) e
k0
j 2 nk
N
j 2 nk
N
,
,
k 0 ,., N 1
n 0 ,., N 1 .
2-
6. Image Restoration
Chapter 5 (Image Restoration, 5.1-5.3, 5.5-5.8)
Image Restoration
A process that attempts to reconstruct or
recover an image that has been degraded
by using some a priori knowledge of the
degradation phenomenon.
Degradation Model
T
5. Edge Detection, Image Segmentation,
and Mathematical Morphology
Chapter 7 (Image Segmentation)
Chapter 8 pp.518-528 (Morphology)
1
Edge Detection Using Derivative Operators
Edges: the image portions which have large gradients
Magnitude of the gradien
4. Image Enhancement in the
Frequency Domain
Chapter 4 (Image Enhancement in the
Frequency Domain)
1
Fourier Transform
2
Fourier Transform
Fourier Transform of a real function is generally complex
F ( f ( x ) F ( u )
f ( x ) exp[ j 2 ux ] dx R ( u ) jI
3. Image Enhancement Using Filtering
In the Spatial Domain
Chapter 3 (Image Enhancement in the
Spatial Domain)
Filtering for:
- Removing noise
- Sharpening image
- Detecting edges
1
1-D Linear Time Invariant (LTI) Systems
1-D signal x(n) passes through a
3. Image Enhancement
Chapter 3 (Image Enhancement in the Spatial Domain)
Section 6.3 (Pseudocolor Image Processing)
1
Gray Level Transformations: Negative
L-1
Image Negative: reverse
the brightness from black
to white. Useful in
displaying medical image