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.
Introduction to digital image processing, image processing, digital image, analog image, formation of digital image, level of digital image processing, components of a digital image processing system, advantages of digital image processing, limitations of digital image processing, fields of digital image processing, ultrasound imaging, x-ray imaging, SEM, PET, TEM
Edge detection is the name for a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities.
Introduction to digital image processing, image processing, digital image, analog image, formation of digital image, level of digital image processing, components of a digital image processing system, advantages of digital image processing, limitations of digital image processing, fields of digital image processing, ultrasound imaging, x-ray imaging, SEM, PET, TEM
Edge detection is the name for a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities.
Here in the ppt a detailed description of Image Enhancement Techniques is given which includes topics like Basic Gray level Transformations,Histogram Processing.
Enhancement using Arithmetic/Logic Operations.
image averaging and image averaging methods.
Piecewise-Linear Transformation Functions
Image enhancement is one of the challenging issues in image processing. The objective of Image enhancement is to process an image so that result is more suitable than original image for specific application. Digital image enhancement techniques provide a lot of choices for improving the visual quality of images. Appropriate choice of such techniques is very important. This paper will provide an overview and analysis of different techniques commonly used for image enhancement. Image enhancement plays a fundamental role in vision applications. Recently much work is completed in the field of images enhancement. Many techniques have previously been proposed up to now for enhancing the digital images. In this paper, a survey on various image enhancement techniques has been done.
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
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.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
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.
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.
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3. INTRODUCTION:
An image defined in the “real
world” is considered to be a function of
two real variables, for example, a(x,y)
with a as the amplitude (e.g. brightness) of
the image at the real coordinate position
(x,y)
Image processing is the study of
any algorithm that takes an image as input
and returns an image as output. It includes
the following:
1. Image display and printing
2. Image editing and
manipulation
3. Image enhancement
4. Feature detection
5. Image compression.
JPEG
Original
Compression
4. WHY IMAGE ENHANCEMENT?
The aim of image enhancement is to improve the visual appearance of an
image, or to provide a “better transform representation for future automated
image processing.
Many images like medical images, satellite images, aerial images and e
v
e
nreal life
photographs suffer from poor contrast and noise.
It is necessary to enhance the contrast and remove the noise to increase i
m
a
g
e
quality.
Enhancement techniques which improves the quality (clarity) of images for
human viewing, removing blurring and noise, increasing contrast, and
revealing details are examples of enhancement operations.
5. WHAT IS IMAGE ENHANCEMENT?
Image enhancement process consists of a collection of techniques t
h
a
t seek to
improve the visual appearance of an image or to convert the image to a
form better suited for analysis by a human or machine.
The principal objective of image enhancement is to modify attributes o
fan
image to make it more suitable for a given task and a specific observer.
Enhancement
Technique
Input Image “Better” Image
Application specific
6. IMAGE ENHANCEMENT
TECHNIQUES:
The existing techniques of image enhancement can be classified into
two categories:
• Spatial domain enhancement
• Frequency domain enhancement.
7. EXAMPLES OF IMAGE ENHANCEMENT
TECHNIQUES:
1. Noise removal
Noisy image De-noised image
2. Contrast adjustment
Low contrast Original contrast High contrast
8. SPATIAL DOMAIN ENHANCEMENT:
x
•Spatial domain techniques are performed to
the image plane itself and they are based on
direct manipulation of pixels in an image.
• The operation can be formulated as
g(x,y)=T[f(x,y)], where g is the output, f is the
input image and T is an operation on f defined
over some neighbourhood of (x,y).
•According to the operations on the image
pixels, it can be further divided into 2
categories:
oPoint operations and
oSpatial operations (including linear and non-
linear operations).
9. ENHANCEMENT METHODS:
1.Contrast stretching :
•Low-contrast images can result from poor illumination, lack of dynamic range in
the image sensor, or even wrong setting of a lens aperture.
•The idea behind contrast stretching is to increase the dynamic range of the gray
levels in the image being processed.
• The general form is:
s =
1+ (m / r) E
where, r are the input image values, s are the output image
values, m is the thresholding value and E the slope.
1
10. Figure shows the effect of the variable E:
• If E = 1 the stretching became a threshold transformation.
• If E > 1 the transformation is defined by the curve which is smoother and
• When E < 1 the transformation makes the negative and also stretching.
11. 2. Noise reduction :
This is accomplished by averaging and median filtering. These
are as follows:
a. Median Filtering :
• The median filter is normally used to reduce noise in an image by
preserving useful detail in the image.
• The median filter considers each pixel in the image in turn and looks at its
nearby neighbors to decide whether or not it is representative of its
surroundings.
• The median is calculated by first sorting all the pixel values from the
surrounding neighborhood into numerical order and then replacing the
pixel being considered with the middle pixel value.
12. Figure below illustrates an example calculation.
b.Noise removal using Averaging:
• Image averaging works on the assumption that the noise in your image is
truly random.
• This way, random fluctuations above and below actual image data will
gradually even out as one averages more and more images.
13. Median filtering
This kind of the noise are called salt
and pepper noise
If we apply smooth filtering we cant
remove the noise.
Median filtering steps
18. 3. Intensity Adjustment :
•
•
•
Intensity adjustment is a technique for mapping an image's intensity values to
a new range.
For example, rice.tif. is a low contrast image. The histogram of rice.tif, shown
in Figure below, indicates that there are no values below 40 or above 225. If
you remap the data values to fill the entire intensity range [0, 255], you can
increase the contrast of the image.
You can do this kind of adjustment with the imadjust function. The general
syntax of imadjust is
J = imadjust(I,[low_in high_in],[low_out high_out])
19. 4. Histogram equalization:
•
•
Histogram Equalization is a technique that generates a gray map which
changes the histogram of an image and redistributing all pixels values to be as
close as possible to a user – specified desired histogram.
It allows for areas of lower local contrast to gain a higher contrast.
Figure above shows the original image and its histogram, and the equalized
versions. Both images are quantized to 64grey levels.
20. Use of Histogram Equalization
Manipulating Contrast
Brightness
Histogram can control the image Quality
Quality Normalizing Histogram Flat profile
To get a high quality image the histogram should be normalize to be a flat profile
24. 5. Image thresholding:
•
•
•
Thresholding is the simplest segmentation method.
The pixels are partitioned depending on their intensity value T.
Global thresholding, using an appropriate threshold T:
g(x, y) = 1, if f (x, y) > T
0, if f (x, y) <= T
• Imagine a poker playing robot that needs to visually interpret the cards in its
hand:
Original Image Thresholded Image
25.
26.
27. If you get the threshold wrong the results can be disastrous:
Threshold Too High Threshold Too Low
28. 6. Grey level slicing
•
•
Grey level slicing is the spatial domain equivalent to band-pass
filtering.
A grey level slicing function can either emphasize a group of intensities
and diminish all others or it can emphasize a group of grey levels and
leave the rest alone.
The figure above shows An example of gray level slicing with and without
background
29. 7. Image rotation:
• Image rotation in the digital domain is a form of re-sampling but is
performed on non-integer points.
•The equation below gives the coordinate transformation in terms of rotation
of the coordinate axis.
Sx = Dx cos(θ) + Dy sin(θ)
Sy = -Dx sin(θ) + Dy cos (θ)
Where, S and D represent source and destination coordinates.
0° rotation 90° rotation 180° rotation
30. CONVERSION METHODS:
1.Greyscale conversion:
•Conversion of a colour image into a greyscale image inclusive of salient features
is a complicated process.
•The converted greyscale image may lose contrasts, sharpness, shadow, and
structure of the colour image.
•To preserve these salient features, the colour image is converted into greyscale
image using three algorithms as stated:
a. The lightness method averages the most prominent and least prominent
colors: (max(R, G, B) + min(R, G, B)) / 2.
b. The average method simply averages the values: (R + G + B) / 3.
c. The luminosity method is a more sophisticated version of the average
method. The formula for luminosity is 0.21 R + 0.71 G + 0.07 B.
34. Average method
Average method is the most simple one. You just have to
take the average of three colors. Since its an RGB image, so
it means that you have add R with G with B and then divide it
by 3 to get your desired grayscale image.
grayscale_average_img = np.mean(fix_img, axis=2)
35. The luminosity method
This method is a more sophisticated version of the average
method. It also averages the values, but it forms a weighted
average to account for human perception
According to this equation, Red has contribute 21%, Green has
contributed 72% which is greater in all three colors and Blue has
contributed 7%.
36. 2. Image File Format:
•
•
The file format is critical to the preservation of an image.
The TIFF file (tagged image file format) is the current preservation format
because it holds all the preservation information required to create a digital
master of the original.
Some of the file formats are: TIFF Preferred Archival format, JPEG
Irreversible image compression, DNG Universal camera raw format etc.
Original JPEG Compression
37. RESOURCES REQUIRED:
Software requirements:
1. Windows Operating System XP and above.
2. MATLAB 7.10.0(R2010a)
Hardware requirements:
1. Hard disk: 16GB and above.
2. RAM: 1GB and above.
3. Processor: Dual-core and above.
46. • The material presented is representative of spatial domain
technique commonly used in practice for image enhancement.
• This area of image processing is a dynamic field, and new
technique and applications are reported routinely in professional
literature and in new product announcement.
•In addition to enhancement, this serves the purpose of introducing a
number of concepts such as intensity adjustment, contrast stretching,
noise filtering, etc. that will be useful in various fields.
CONCLUSION: