This document discusses various methods for contrast enhancement of digital images. It defines contrast and describes how contrast is important for distinguishing objects from backgrounds. Two main categories of contrast enhancement methods are described: spatial domain methods, which operate directly on pixel values; and frequency domain methods, which modify the Fourier transform of an image. Specific spatial techniques covered include logarithmic transformation, power law transformation, gamma correction, and histogram equalization. Contrast stretching is also discussed. Examples of code implementations and results are provided. Contrast enhancement has applications in medical imaging, surveillance, and other fields where improving image interpretability is important.
Image enhancement is the process of adjusting digital images so that the results are more suitable for display or further image analysis. For example, you can remove noise, sharpen, or brighten an image, making it easier to identify key features.
Here are some useful examples and methods of image enhancement:
Filtering with morphological operators, Histogram equalization, Noise removal using a Wiener filter, Linear contrast adjustment, Median filtering, Unsharp mask filtering, Contrast-limited adaptive histogram equalization (CLAHE). Decorrelation stretch
Image segmentation techniques
More information on this research can be found in:
Hussein, Rania, Frederic D. McKenzie. “Identifying Ambiguous Prostate Gland Contours from Histology Using Capsule Shape Information and Least Squares Curve Fitting.” The International Journal of Computer Assisted Radiology and Surgery ( IJCARS), Volume 2 Numbers 3-4, pp. 143-150, December 2007.
Lecture 1 for Digital Image Processing (2nd Edition)Moe Moe Myint
-What is Digital Image Processing?
-The Origins of Digital Image Processing
-Examples of Fields that Use Digital Image Processing
-Fundamentals Steps in Digital Image Processing
-Components of an Image Processing System
Image Enhancement: Introduction to Spatial Filters, Low Pass Filter and High Pass Filters. Here Discussed Image Smoothing and Image Sharping, Gaussian Filters
Image enhancement is the process of adjusting digital images so that the results are more suitable for display or further image analysis. For example, you can remove noise, sharpen, or brighten an image, making it easier to identify key features.
Here are some useful examples and methods of image enhancement:
Filtering with morphological operators, Histogram equalization, Noise removal using a Wiener filter, Linear contrast adjustment, Median filtering, Unsharp mask filtering, Contrast-limited adaptive histogram equalization (CLAHE). Decorrelation stretch
Image segmentation techniques
More information on this research can be found in:
Hussein, Rania, Frederic D. McKenzie. “Identifying Ambiguous Prostate Gland Contours from Histology Using Capsule Shape Information and Least Squares Curve Fitting.” The International Journal of Computer Assisted Radiology and Surgery ( IJCARS), Volume 2 Numbers 3-4, pp. 143-150, December 2007.
Lecture 1 for Digital Image Processing (2nd Edition)Moe Moe Myint
-What is Digital Image Processing?
-The Origins of Digital Image Processing
-Examples of Fields that Use Digital Image Processing
-Fundamentals Steps in Digital Image Processing
-Components of an Image Processing System
Image Enhancement: Introduction to Spatial Filters, Low Pass Filter and High Pass Filters. Here Discussed Image Smoothing and Image Sharping, Gaussian Filters
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 is the basic introductory presentation for beginners. It gives you the idea about what is image processing means. The presentation consists of introduction to digital image processing, image enhancement, image filtering, finding an image edge, image analysis, tools for image processing and finally some application of digital image processing.
At the end of this lesson, you should be able to;
describe the energy and the EM spectrum.
describe image acquisition methods.
discuss image formation model.
express sampling and quantization.
define dynamic range and image representation.
Image Segmentation
Types of Image Segmentation
Semantic Segmentation
Instance Segmentation
Types of Image Segmentation Techniques based on the image properties:
Threshold Method.
Edge Based Segmentation.
Region-Based Segmentation.
Clustering Based Segmentation.
Watershed Based Method.
Artificial Neural Network Based Segmentation.
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 is the basic introductory presentation for beginners. It gives you the idea about what is image processing means. The presentation consists of introduction to digital image processing, image enhancement, image filtering, finding an image edge, image analysis, tools for image processing and finally some application of digital image processing.
At the end of this lesson, you should be able to;
describe the energy and the EM spectrum.
describe image acquisition methods.
discuss image formation model.
express sampling and quantization.
define dynamic range and image representation.
Image Segmentation
Types of Image Segmentation
Semantic Segmentation
Instance Segmentation
Types of Image Segmentation Techniques based on the image properties:
Threshold Method.
Edge Based Segmentation.
Region-Based Segmentation.
Clustering Based Segmentation.
Watershed Based Method.
Artificial Neural Network Based Segmentation.
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.
The application of image enhancement in color and grayscale imagesNisar Ahmed Rana
This is the presentation which was presented at All Pakistan Technical Paper Competition Lahore under the title "The application of image enhancement in color and grayscale images"
IMAGE ENHANCEMENT IN CASE OF UNEVEN ILLUMINATION USING VARIABLE THRESHOLDING ...ijsrd.com
Uneven illumination always affects the visual quality images which results in poor understanding about the content of the images. There is no accepted universal image enhancement algorithm or specific criteria which can fulfill user needs. The processed image may be very different with the original image in the visual effects, but it also may be similar to the original image [1]. It will be a developing tradition to integrate the advantage of various algorithms to practical application to image enhancements [2]. Zhang et al. [3] presents an adaptive image contrast enhancement method. The proposed method is based on a local gamma correction piloted by histogram analysis. In this paper , to avoid uneven Illuminance image is divided into different segments . It works locally to decrease contrast as if we perform enhancement techniques globally on portions which are already bright then this gives poor results. Enhancement techniques are applied only to those dark portions. We need accurate method that not only enhance the image but also preserve the information.
MATLAB is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages
Seam Carving Approach for Multirate Processing of Digital ImagesIJLT EMAS
This paper presents a new approach called S eam Carving for multirate signal processing of digital images. Increasing and decreasing the sizes of digital images are common place in day-today image processing. These methods involve long procedures and sometimes consume more time for getting implemented. Whereas, using the S eam Carving method eradicates the excess time involved in upsampling and downsampling a digital image considerably as this method is straightforward and simple to implement. This method comes in handy when we are dealing with large medical images and remotely sensed images. This technique is applied on standard images and its performance is analyzed. The entire work was implemented using Matlab R2017a software package.
Fpga implementation of fusion technique for fingerprint applicationIAEME Publication
Image Fusion is a process of combining relevant information from a set of images, into a
single image, wherein the resultant fused image will be more informative and complete than any of
the input images. This paper discusses Laplacian Pyramid (LP) based image fusion techniques for
fingerprint application. The technique is implemented in MatLab and evaluation parameters Mean
Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Matching score are discussed. As well
the same implemented on Virtex-5 FPGA development board using Verilog HDL. LP based
technique provides better results for image fusion than other techniques.
Image Quality Feature Based Detection Algorithm for Forgery in Images ijcga
The verifying of authenticity and integrity of images is a serious research issue. There are various types of techniques to create forged images for various intentions. In this paper, Attempt is made to verify the authenticity of image using the image quality features like markov and moment based features. They are found to have their best results in case of forgery involving splicing.
AN EMERGING TREND OF FEATURE EXTRACTION METHOD IN VIDEO PROCESSINGcscpconf
Recently the progress in technology and flourishing applications open up new forecast and defy
for the image and video processing community. Compared to still images, video sequences
afford more information about how objects and scenarios change over time. Quality of video is
very significant before applying it to any kind of processing techniques. This paper deals with
two major problems in video processing they are noise reduction and object segmentation on
video frames. The segmentation of objects is performed using foreground segmentation based
and fuzzy c-means clustering segmentation is compared with the proposed method Improvised
fuzzy c – means segmentation based on color. This was applied in the video frame to segment
various objects in the current frame. The proposed technique is a powerful method for image
segmentation and it works for both single and multiple feature data with spatial information.
The experimental result was conducted using various noises and filtering methods to show which is best suited among others and the proposed segmentation approach generates good quality segmented frames.
A Report on Bidirectional Visitor Counter using IR sensors and Arduino Uno R3Abhishekvb
The aim of our project is to make a controller which can sense if any person enters the room and it lights up the room automatically and also counts how many person are entering the room or going out of it.
Bidirectional Visitor Counter using IR sensors and Arduino Uno R3Abhishekvb
The aim of our project is to make a controller which can sense if any person enters the room and it lights up the room automatically and also counts how many person are entering the room or going out of it.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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/
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
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
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
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.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
2. ANALYSIS OF CONTRAST ENHANCEMENT
METHODS
Contrast is the difference in
visual properties that makes
an object (or image)
distinguishable from other
objects and the
background.
It is the different between
the darker and the lighter
pixel of the image, if it is big
the image will have high
contrast and in the other
case the image will have
low contrast.
CONTRAST DEFINITION
3. CONTRAST ENHANCEMENT METHODS
The principal objective of enhancement is to process an
image so that the result is more suitable than the
original image for a specific application.
For example, a method that is quite useful for
enhancing X-ray images may not necessarily be the
best approach for enhancing pictures of Mars
transmitted by a space probe.
5. SPATIAL DOMAIN METHODS
The term spatial domain refers to the image plane
itself.
Spatial domain methods are procedures that
operate directly on these pixels in an image.
Spatial domain processes will be denoted by the
expression g(x,y)=T[f(x,y)], where f(x,y) is the input
image, g(x,y) is the processed image, and T is an
operator on f, defined over some neighborhood of
(x, y).
6. FREQUENCY DOMAIN METHODS
Frequency domain processing techniques are
based on modifying the Fourier transform of an
image.
More suitable for filtering spectrums.
Any function that periodically repeats itself can be
expressed as the sum of sines and cosines of
different frequencies, each multiplied by a different
coefficient.
7. LOGARITHMIC TRANSFORMATION
The general form is
s = c * log (1 + r),
where s is the output value, r is the input value and
c is a constant.
This transformation maps a narrow range of low
gray-level values in the input image into a wider
range of output levels.
MATHEMATICAL MODELING
8. FLOW CHART FOR IMPLEMENTATION OF
LOGARITHMIC TRANSFORMATION
9. CODE FOR LOGARITHMIC TRANSFORMATION
im=imread('cameraman.tif');
subplot(231),imshow(im);
title('original image');
imd=im2double(im);
c=2.5;
d=0.5;
im3=c*log(1+imd);
im4=d*log(1+imd);
subplot(232),imshow(im3);
title('transformed image(c=2.5)');
subplot(233),imshow(im4);
title('transformed image(c=0.5)');
subplot(234),imhist(im);
title('histogram of the original image');
subplot(235),imhist(im3);
title('histogram of the transformed image(c=2.5)');
subplot(236),imhist(im4);
title('histogram of the transformed image(c=0.5)');
11. POWER-LAW TRANSFORMATION
The general form is s = c * 𝐫 𝜸
,
where c and γ are positive
constants.
Power-law curves with
fractional values of γ map a
narrow range of dark input
values into a wider range of
output values, with the
opposite being true for higher
values of input levels.
12. FLOW CHART FOR IMPLEMENTATION OF
POWER LAW TRANSFORMATION
13. CODE FOR POWER LAW TRANSFORMATION
im=imread('cameraman.tif');
subplot(231),imshow(im);
title('original image');
imd=im2double(im);
gamma=0.25;
im3=imd.^gamma;
gamma=2.5;
im4=imd.^gamma;
subplot(232),imshow(im3);
title('transformed image(gamma=0.25)');
subplot(233),imshow(im4);
title('transformed image(gamma=2.5)');
subplot(234),imhist(im);
title('histogram of the original image');
subplot(235),imhist(im3);
title('histogram of the transformed image(gamma=0.5)');
subplot(236),imhist(im4);
title('histogram of the transformed image(gamma=2.5)');
15. GAMMA CORRECTION
The exponent in the
power-law equation is
referred to as gamma.
The process used to
correct this power-law
response phenomena
is called gamma
correction.
The process used to
correct power-law
response phenomena
is called gamma
correction.
𝟏. 𝟖 < 𝛄 < 2.5
16. HISTOGRAM EQUALIZATION
The general form is
sk=
L−1 ∗(rk−rkmin)
rkmax−rkmin
where
k=0,1,2,…L-1, r and s are
the input and output pixels of
the image, L is the different
values that can be the pixels,
and rkmax and rkmin are the
maximum and minimum gray
values of the input image.
This method usually increase
the global contrast of the
image. This allows for area’s
of lower contrast to gain
higher contrast.
20. ADVANTAGES
The method is useful in images with backgrounds and
foregrounds that are both bright or both dark.
A advantage of the method is that it is a fairly
straightforward technique and an invertible operator.
DISADVANTAGE
A disadvantage of the method is that it is indiscriminate.
It may increase the contrast of background noise, while
decreasing the usable signal.
21. CONTRAST STRETCHING
Low-contrast images can result from poor
illumination, lack of dynamic range in the imaging
sensor, or even wrong setting of a lens aperture
during image acquisition.
The idea behind contrast stretching is to increase
the dynamic range of the gray levels in the image
being processed.
25. APPLICATION
(Left) Original sensed fingerprint; (center) image
enhanced by detection and thinning of ridges;
(right) identification of special features called
minutia", which can be used for matching to millions
of fingerprint representations in a database.
26. CONCLUSION
Image enhancement is basically improving the
interpretability or perception of information in images for
human viewers and providing `better' input for other
automated image processing techniques.
For dark images with low contrast the better results will be
with the logarithm and the power law transformations using
in the second one gamma values lower than 1.
For light images it would be use the power law
transformation with gamma higher than 1.
For image with low contrast in gray scale the better methods
are histogram equalization and contrast stretching.