Histogram equalization is a method in image processing of contrast adjustment using the image's histogram. Histogram equalization can be used to improve the visual appearance of an image. Peaks in the image histogram (indicating commonly used grey levels) are widened, while the valleys are compressed.
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
Histogram Processing
Histogram Equalization
Histogram Matching
Local Histogram processing
Using histogram statistics for image enhancement
Uses for Histogram Processing
Histogram Equalization
Histogram Matching
Local Histogram Processing
Basics of Spatial Filtering
its very useful for students.
Sharpening process in spatial domain
Direct Manipulation of image Pixels.
The objective of Sharpening is to highlight transitions in intensity
The image blurring is accomplished by pixel averaging in a neighborhood.
Since averaging is analogous to integration.
Prepared by
M. Sahaya Pretha
Department of Computer Science and Engineering,
MS University, Tirunelveli Dist, Tamilnadu.
This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.
After an image has been segmented into regions ; the resulting pixels is usually is represented and described in suitable form for further computer processing.
Histogram Processing
Histogram Equalization
Histogram Matching
Local Histogram processing
Using histogram statistics for image enhancement
Uses for Histogram Processing
Histogram Equalization
Histogram Matching
Local Histogram Processing
Basics of Spatial Filtering
its very useful for students.
Sharpening process in spatial domain
Direct Manipulation of image Pixels.
The objective of Sharpening is to highlight transitions in intensity
The image blurring is accomplished by pixel averaging in a neighborhood.
Since averaging is analogous to integration.
Prepared by
M. Sahaya Pretha
Department of Computer Science and Engineering,
MS University, Tirunelveli Dist, Tamilnadu.
This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.
After an image has been segmented into regions ; the resulting pixels is usually is represented and described in suitable form for further computer processing.
What are the 3 principles of information security?
When we discuss data and information, we must consider the CIA triad. The CIA triad refers to an information security model made up of the three main components: confidentiality, integrity and availability. Each component represents a fundamental objective of information security.
An evaluation of two popular segmentation algorithms, the mean shift-based segmentation algorithm and a graph-based segmentation scheme. We also consider a hybrid method which combines the other two methods.
COM2304: Intensity Transformation and Spatial Filtering – I (Intensity Transf...Hemantha Kulathilake
At the end of this lesson, you should be able to;
describe spatial domain of the digital image.
recognize the image enhancement techniques.
describe and apply the concept of intensity transformation.
express histograms and histogram processing.
describe image noise.
characterize the types of Noise.
describe concept of image restoration.
Comparison of Histogram Equalization Techniques for Image Enhancement of Gray...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
3. FLOw OF SEMINAR.
1.WHAT IS A DIGITAL IMAGE?
2.WHAT IS A HISTOGRAM?
3.WHAT IS HISTOGRAM EQUALIZATION?
4.DIFFERENT EQUALIZATION METHODS AND ITS DRAWBACK.
5.HOW DRAWBACK OF EACH METHOD IS RECTIFED?
6. Image matrix
Image
wHAT IS A COLOUR IMAGE?
234 212 123
135 231 233
.
121 222
. .
243 121
.
. . .
112 167
.
. . .
Red matrix
Green matrix
Blue matrix
.
.
7. wHAT IS A HISTOGRAM?
Consider a 5x5 image with integer intensities in the range between zero and seven:
0 7 3 2 3
0 0 0 6 7
7 7 2 2 0
1 1 0 4 1
0 0 7 4 1
Image matrixImage
0 1 2 3 4 5 6 7
Gray scale
Black White
8. wHAT IS A HISTOGRAM?
Consider a 5x5 image with integer intensities in the range between one and eight:
0 7 3 2 3
0 0 0 6 7
7 7 2 2 0
1 1 0 4 1
0 0 7 4 1
Image matrixImage
0 1 2 3 4 5 6 7
Grey scale
Black White
Number of pixel with intensity value 0 [h(r0)] = 8
9. wHAT IS A HISTOGRAM?
0 7 3 2 3
0 0 0 6 7
7 7 2 2 0
1 1 0 4 1
0 0 7 4 1
Image matrixImage
0 1 2 3 4 5 6 7
Grey scale
Black White
Number of pixel with intensity value 0 [h(r0)] = 8
Similarly for 1 h(r1) = 4
10. wHAT IS A HISTOGRAM?
0 7 3 2 3
0 0 0 6 7
7 7 2 2 0
1 1 0 4 1
0 0 7 4 1
Image matrixImage
Similarly
INTENSITY r 0 1 2 3 4 5 6 7
NUMBER of
pixels of r
h(r)
h(r0)=8 h(r1)=4 h(r2)=3 h(r3)=2 h(r4)=2 h(r5)=0 h(r6)=1 h(r7)=5
11. r
wHAT IS A HISTOGRAM?
Image matrix
0 1 2 3 4 5 6 7
HISTOGRAM
Intensity values
Number of pixels of
intensity r
r 0 1 2 3 4 5 6 7
h(r) 8 4 3 2 2 0 1 5
Histogram plots the number of pixels for each intensity value.
h(r)
12. What is a histogram?
r 0 1 2 3 4 5 6 7
h(r) 8 4 3 2 2 0 1 5
p(r)
h(r)/(5*5)
8/25 4/25 3/25 2/25 2/25 0/25 1/25 5/25
HISTOGRAM - h(r) - Y axis - number of intensities
NORMALIZED HISTOGRAM - p(r) - Y axis - probability of intensities
13. SAMPLE IMAGES AND ITS HISTOGRAM
Bright image
Intensity range 0 - 255
14. SAMPLE IMAGES AND ITS HISTOGRAM
Bright image
Intensity range 0 - 255
0 50 100 150 200 255
Intensity
No:ofpixels
DARK BRIGHT
h(r)
26. GLOBAL HISTOGRAM EQUALIZATION
OBTAIN
HISTOGRAM
OBTAIN PDF
OBTAIN CDF
OBTAIN
TRANSFORMATIO
N FUNCTION
MAPPING OF NEW
INTENSITY VALUES
NEW HISTOGRAM
Original histogram
M*N
PDF
1..
CDF
1
x0
XL-1
O/P
x0
XL-1
MappingTransformation
function
t1
t2
t2
New histogram
t1t1 t2
t2t1t2t1
29. THEORY OF BIHISTOGRAM EQUALIZATION
HISTOGRAM EQUALIZED SEPERATELY AROUND MEAN.
THUS CONSERVE THE MEAN.
ORIGINAL HISTOGRAM BIHISTOGRAM EQUALIZED
30. BIHISTOGRAM EQUALIZATION
OBTAIN PDF
(lower subimage)[X0-Xm]
OBTAIN CDF
OBTAIN
TRANSFORMATIO
N FUNCTION
MAPPING OF NEW
INTENSITY VALUES
NEW HISTOGRAM
DIVIDE HISTOGRAM WITH RESPECT TO
INTENSITY MEAN (X m ).
OBTAIN
HISTOGRAM
OBTAIN PDF
(upper subimage)[Xm-Xl-1]
OBTAIN CDF
OBTAIN
TRANSFORMATIO
N FUNCTION
MAPPING OF NEW
INTENSITY VALUES
+
GHE
GHE
Partition
Merging
33. THOERY OF BIHISTOGRAM EQUALIZATION
WITH A PLATEAU LIMIT .
BIHISTOGRAM CLIPPING HISTOGRAM
ABOVE PLATEAU LIMIT
TL PLATEAU LIMITS FOR LOWER HISTOGRAM.
TU PLATEAU LIMITS FOR UPPER HISTOGRAM.
SELECT PLATEAU LIMIT
34. BIHISTOGRAM EQUALIZATION WITH A
PLATEAU LIMIT
OBTAIN PDF
(lower subimage)[X0-Xm]
OBTAIN CDF
OBTAIN
TRANSFORMATION
FUNCTION
MAPPING OF NEW
INTENSITY VALUES
NEW HISTOGRAM
DIVIDE HISTOGRAM WITH RESPECT TO
INTENSITY MEAN (X m ).
OBTAIN
HISTOGRAM
OBTAIN PDF
(upper subimage)[Xm-Xl-1]
OBTAIN CDF
OBTAIN
TRANSFORMATION
FUNCTION
MAPPING OF NEW
INTENSITY VALUES
+
GHE
GHE
Partition
Merging
CLIP WRT
AMPLITUDE MEAN
CLIP WRT
AMPLITUDE MEAN
Clipping
39. CONCLUSIONHistogram?
IN AN IMAGE
NOTHING WORSE MORE THAN LOW CONTRAST
GLOBAL HISTOGRAM EQUALIZATION
NOTHING WORSE MORE THAN MEAN CONSERVATION
BI-HISTOGRAM EQUALIZATION
NOTHING WORSE MORE
THAN ………………?
NOTHING WORSE MORE THAN LEVEL SATURATION
BI-HISTOGRAM EQUALIZATION WITH PLATEAU LIMIT
40. REFERENCESStogram?
Bi-Histogram Equalization with a Plateau Limit
for Digital Image Enhancement
Chen Hee Ooi, Student Member, IEEE, Nicholas Sia Pik Kong,
Student Member, IEEEand Haidi Ibrahim, Member, IEEE
IEEE Transactions on Consumer Electronics, Vol. 55, No. 4,
NOVEMBER 2009
Contrast Enhancement Using Brightness Preserving
Bi-Histogram Equalization
YEONG-TAEG KIM, MEMBER, IEEE
Color Image Enhancement Using Brightness Preserving
Dynamic Histogram Equalization
Nicholas Sia Pik Kong, Student Member, IEEE, and Haidi
Ibrahim, Member, IEEE.
Preserving brightness in histogram equalization
based contrast enhancement techniques
Soong-Der Chen a, Abd. Rahman Ramli
Digital image processing by Gonzalez and Woods
42. • Lorem ipsum dolor sit amet, consectetuer
adipiscing elit. Vivamus et magna. Fusce sed
sem sed magna suscipit egestas.
• Lorem ipsum dolor sit amet, consectetuer
adipiscing elit. Vivamus et magna. Fusce sed
sem sed magna suscipit egestas.
1
43. • Lorem ipsum dolor sit amet, consectetuer
adipiscing elit. Vivamus et magna. Fusce sed
sem sed magna suscipit egestas.
• Lorem ipsum dolor sit amet, consectetuer
adipiscing elit. Vivamus et magna. Fusce sed
sem sed magna suscipit egestas.
1
