A brief of how digital image processing works

1. A review on Digital Image Processing
Mr.Bhuvan Jain.A Mr.Aman Jain Ms.Yashika Saini
Student Student Professor
Department of CSE Department of IT Department of CSE
AIET, Jaipur AIET, Jaipur AIET, Jaipur
bhuvanjain100@gmail.com amanjainaj7200@gmail.com Yashikasaini78@gmail.com
Information on the manuscript:-
Image Processing, DIP, Image
Restoration, Image Segmentation,
Image Enhancement, Image
Processing Algorithms SIFT, SURF,
BRIEF ORB.
Abstract:
Image processing is a multi-step process
that uses a variety of techniques and
algorithms. Because there is no set pattern,
there are limitations to using image
processing in any process. The current
study is a comprehensive examination of
the many uses of image processing
techniques in several sectors of modern
engineering. Digital image processing
(DIP) is utilized in a variety of
applications, including video editing,
office biometric systems, endoscopy,
quality control in the textile and other
manufacturing industries, digital
photograph enhancement, signature
verification, and so on. It also covers a
wide range of image processing techniques
such as picture acquisition, image
segmentation, image modification, image
restoration, and image compression. The
benefits and drawbacks of image
processing techniques and algorithms such
as SIFT; SURF, BRIEF, and ORB are
thoroughly explored.
Introduction:
Figure 1:- Digital Image Processing
Analog image processing (AIP) and digital
image processing (DIP) are the two forms
of image processing (DIP). Hard copies,
such as printouts and pictures, may be
scanned using AIP. When utilizing these
visual approaches, image analysts employ
a variety of interpretive foundations.
Image processing is done using computer
algorithms in DIP. AIP is favored over
digital image processing.
There are a variety of algorithms that may
be applied with the input data.
 Signal problems like as noise and
distortion are avoided. DIP has
become an indispensable element
of modern engineering and is
favored over AIP [1]. It is quick,
efficient, and adaptable.
 The focus of image processing is
on the target region to be
investigated as well as the analyst's
knowledge. Another significant
element in image processing via
visual approaches is association.
 As a result, human knowledge and
collateral data must be used to

2. image processing throughout the
study. Because image processing is
closely connected to artificial
vision or simulated vision, it is
likely to achieve at least one of the
aims.
 Hallucination Means keeping track
of and characterizing objects that
do not exist in reality but whose
characteristics are stated.
 Image restoration and/or
sharpening to increase image
quality (contrast, brightness,
colour, hue, sharpness, and so on).
 3. Image repossession here we look
for the image that we're looking
for. The characteristics of the
intended picture, such as form,
colures, and pattern, are specified,
and we search for the required
image among the photos supplied.
 Pattern measurement this is done to
determine the number of items in a
specific picture or region.
 Acknowledgment of the wizard
this is a technique for
distinguishing particular items in a
picture.
 We must examine all conceivable
approaches, their pros and
downsides, because there are no set
techniques for all applications.
Each approach has its own set of
benefits and drawbacks. The
evaluation includes a discussion of
a variety of applications.
Application of Image
Processing:
Image processing has a variety of
applications, as listed below.
 Image sharpening and
restoration is the process of
improving an image's aesthetic
impact in order to increase its
information content. This entails
contrasting and/or sharpening of
edges/boundaries. Contrast
enhancement techniques have been
created and used to a variety of
image processing issues. A few are
listed below.
1. Enhancement of contrast
2. Changes in intensity, colour, and
saturation
3. Slicing of density
4. Enhancement of the edges
5. Making digital mosaics
6. Producing synthetic stereo images
7. Noise removal using a Wiener filter
8. Linear contrast adjustment
9. Median filtering
10.Unsharp mask filtering
 Character Recognized:
Character recognition can help speed up
the processing of scanned pictures. It
recognizes and extracts text content from a
variety of data fields. When we scan a
form and process it with document
imaging software, for example, OCR
(Optical Character Recognition) allows us
to transmit data from the document
straight to an electronic database. OCR
transforms typewritten or printed text into
binary data from scanned or picture
images. It is a method of digitizing printed
manuscripts in order to do tasks like
editing, searching, text-to-speech
conversion, key data extraction, and text
mining. Previously, these automations
were carried out using pictures of each
character. This limited the number of
typefaces that may be used at once. The
modern age has given typefaces a lot of
flexibility in terms of size, type, and
orientation. Some commercial techniques
may duplicate formatted output that
closely resembles the original scanned
window, including columns, pictures, and
other non-textual components.
 Target Recognition:
ATDR identifies, classifies, and tracks
target objects contained in a picture
produced by laser radar (LR), synthetic-

3. aperture radar (SAR), or an infrared or
video camera. ATDR transforms the
sensor's signal into a digital picture, and
then extracts a coarse contour or outline of
the target item to distinguish it from the
backdrop or surrounding region.
Finally, it matches the characteristics
characterizing the target item to identify
the object. For picture segmentation, a
variety of approaches and algorithms have
been developed, including I edge detection
methods, (ii) region splitting and (iii)
region growing methods, and (IV)
clustering methods. The ATR process
employs a variety of algorithms and
approaches, but no one approach has yet
shown to be adequate. By combining
multiple techniques, we can create a high-
performance ATR system.
 Pattern recognition:
Objects or pictures are recognized in this
kind by matching them to different
patterns supplied. The matching is based
on similarities in shape, colour, and other
factors. The 'finding Wolds issue' is the
best illustration.
 Biometric Identification:
Biometrics include measurements of
human features such as fingerprints, hand
geometry, signatures, retina and iris
patterns, voice waves, DNA, and so on.
The following are examples of new and
developing biometric techniques:
1. Smell recognition in humans
2. Biometrics of the EEG
3. Spectroscopy of the skin
4. Texture of the knuckles
5. Recognition of fingernails
Table 1 shows the results of a comparison
of several biometric methods. Fingerprint
identification is the most prevalent
biometric recognition method. Every
person has their own fingerprints. Gray
scale image, phase image, skeleton image,
and minutiae are the four forms of
fingerprint representation techniques used
by most fingerprint matching systems. The
minutiae-based representation scheme has
become the most frequently used
fingerprint representation scheme because
to its distinctness, compactness, and
consistency with characteristics utilized by
human fingerprint specialists.
Table of Biometric Identification:-
Biome
trics
Facia
l
Reco
gnitio
n
Iris
sca
n
Fin
ger
pri
nt
Fin
ger
vei
n
Voic
e
Reco
gniti
on
Lips
reco
gniti
on
Accur
acy
Low Hig
h
Me
diu
m
Hig
h
Low Medi
um
Cost High Hig
h
Lo
w
Me
diu
m
Medi
um
Medi
um
Size of
Temp
late
Large Sm
all
Sm
all
me
diu
m
Small Small
Long term
Stabil
ity
Low Me
diu
m
Lo
w
Hig
h
Low Medi
um
Securi
ty
level
Low me
diu
m
Lo
w
Hig
h
Low High
 Feature Extractor:
The basis of fingerprint technology is
feature extraction. The recorded image's
quality is improved by eliminating noise
using a noise reduction algorithm that
analyses the image and detects minutiae.
Points of bifurcation and ridge ends are the
most commonly utilized minutiae in
applications.
 Matcher:
For identification or matching, the
fingerprint pictures are compared to those
in the database (one-to-many or one-to-one
matching).Remote sensing, transmission
encoding, machine vision, colour

4. processing, video processing, and
microscopy are some of the additional
uses.
Techniques of image
processing:
Figure 2 represents different techniques of
image processing.
Figure 2:- Techniques of Image
Processing.
 Image Segmentation:
To aid in the annotation of the object
scene, segmentation is the act of separating
the picture into component areas or
objects. Image segmentation is used to
precisely determine the image's content.
Edge detection is a crucial technique for
picture segmentation in this scenario.
 Region based segmentation:
The division of a picture into regions is a
process (small groups of connected pixels
having similar properties). Images are
interpreted using regions. A region might
be associated with a specific object or
different sections of an entity. In noisy
pictures with difficult-to-detect
boundaries, region-based methods are
typically superior. In region-based
techniques, reasonable accuracy levels are
available.
 Edge based segmentation:
It concentrates on intensity values that are
both discontinuous and comparable. The split
of a picture based on sudden changes in
intensity, such as edges or borders, in the
situation of discontinuous intensity values. In
image analysis, edge detection is a critical
problem. The acquired boundary markings
correspond to the object's edges. As a result,
the item may be separated from the picture
by detecting its edges. When the edge
detection technique is used, the result is a
binary image. The essence of edge-based
techniques is that they are interactive.
There are three fundamental steps in edge
detection:
1. Filtering &Enhancement
2. Detection of edge points
3. Edge localization
 Thresholding:
Figure 3 represents Thresholding
of an image.
Figure 3:-Thresholding of an Image
Thresholding is a basic but effective
method for segmenting pictures with
bright objects against a dark backdrop. It
transforms a multi-level image into a
binary image, divides image pixels into
multiple areas, and separates objects from
background by selecting an appropriate
threshold T. There are two techniques
depending on T: local thresholding (when
T is constant) and global thresholding
(when T is variable) (T have multiple
values). The global thresholding fails if the

5. backdrop lighting is uneven. If the
intensity of any pixel (x, y) is greater than
or equal to the threshold value, it is
considered to be part of the object;
otherwise, it is regarded to be background.
When we use this approach, we have
reduced noise resistance.
 Feature Based segmentation:
Clustering is the act of grouping things
together based on their qualities, so that
each cluster comprises objects that are
similar but not identical to those in other
clusters. Clustering is done utilizing a
variety of techniques and methods for
computing or identifying the cluster. Good
clustering algorithms create high intra-
cluster similarity and low inter-cluster
similarity.
The Clustering methods are classified into
2 types.
K mean clustering:
K-mean is an unsupervised learning
method that solves the well-known
clustering issue. It is quick, resilient, and
simple. The approach involves classifying
a given data set using a predetermined
number of k clusters. When data sets are
different, K-mean clustering methods get
the best results.
Fuzzy C-Mean [FCM] Algorithm:
Fuzzy Clustering is a technique that allows
items to belong to many clusters, each
with its own membership. This is one of
the most successful pattern recognition
methods. The Fuzzy C-Mean is one of the
most often used fuzzy clustering methods.
We may keep the data set's information by
utilizing FCM. In FCM, each cluster
center assigns membership to a data point,
which means that a data point might
belong to more than one cluster center.
 Model based segmentation:
The Markov random field is used in this
approach. Inbuilt region constraints are
utilized for colour segmentation. MRF is
combined with edge detection to determine
edge accuracy. The relationships between
colour components are included in this
technique.
 Image compression:
Compression of picture refers to the
process of reducing the size of digital
image recordings in order to eliminate
duplication and store and transmit data
more effectively. There are two forms of
picture compression: lossy and lossless.
 Lossless compression:
Figure 4:- Lossless compression of
Image
Figure 4 represents different Lossless
compression of Image.It is lossless if the
image quality stays unchanged after
compression. It is mostly used for medical
imaging, technical drawing content, and
archiving reasons, among other things.
 Lossy compression:
When using lossy compression, the data
quality degrades after compression. Lossy
methods are employed in situations when a
small compromise of quality is acceptable
in exchange for fast speed. JPEG is the
most prevalent, as it compresses full
colour or gray scale pictures. In this case,
the image is split into eight by eight
chunks with no overlap between them.
JPEG compresses data using discrete

6. cosine transformations. Wavelet transform
is a different type of compression method.
Wavelet data is split into distinct
frequency components, each of which is
investigated independently. When it comes
to analyzing physical situations, Wavelets
outperform standard Fourier methods.
Classification of Digital Image
Processing:
Classification is used to extract
information from pictures, name them, and
extract pixels from them. There will be a
lot of photos of the same thing. A suitable
scheme and sufficient training samples are
necessary for efficient categorization. The
classification is done based on the needs of
the user. Artificial neural networks, expert
systems, and fuzzy logic are just a few of
the categorization methods available.
There are several different types of
categorization methods, such as per pixel,
sub pixel, and per field. The approach of
'per-pixel categorization' is the most often
utilized. Sub pixel algorithms, which are
made up of a variety of pixel problems,
give a better degree of precision. Data per
field categorization is the best method for
precise 3D resolution.
There are two types of classification
techniques: supervised and unsupervised.
Spectral signatures acquired from training
samples are used to categories’ a picture in
supervised classification. The picture is
classified using multivariate classification
techniques. Unsupervised categorization
relies on the machine's output without any
user input. Pixels from the same category
are clustered together in this approach.
 Image Restoration:
Figure 5 represents different
techniques of Reducing Noisy
Image of an image.
Figure 5:-Reducing Noisy Image
Image restoration is the process of
obtaining a flawless image from a
damaged and noisy image.There are
various processes involved in restoration
of image.
1 Inverse filtering
2 Weiner Filtering
3 Wavelet Restoration
4 Blind De-convolution
 Image enhancement:
It improves the image quality by using
several filters. The restoration of pictures
may be accomplished using two sorts of
models: deterioration and restoration.
Image enhancement is used to improve an
image's suitability for a certain purpose,
such as graphic display.
 Spatial domain enhancement
(SDE):
In which pixel value varies as a function of
intensity of the surrounding.
 Frequency domain enhancement
(FDE):
It may involve the point processing this
involves following steps
1. Intensity transformation
2. Image negative
3. Contrast stretching
4. Grey level slicing
5. Histogram processing
6. Image subtraction
7. Image averaging

7. Figure 6 represents Histogram Images
Conversation of an image
Figure 6:- Histogram Images
Conversation
 Acquisition of image:
Any visualization scheme begins with this
stage. After the image is captured, it is
subjected to a number of procedures.
Essentially, image acquisition is the
process of retrieving pictures from
multiple sources.
 Image Representation:
The term "image representation" refers to
the act of transforming raw data into a
form that can be processed by a computer.
To depict the photos, two sorts of methods
are employed.
 Boundary representation:
It shows the picture's intrinsic form. As a
result, it concentrates on the object's form,
whether it's a corner, rounded, or any other
shape.
 Region representation:
It's used to investigate the internal
characteristics of a picture. There are four
types of image representation depending
on the amount of image processing
through machine: pixel based, block based,
region based, and hierarchical based.
 Image transformation:
Pictures are subjected to arithmetic
operations or sophisticated mathematical
procedures that transform images from one
representation to another. Simple image
arithmetic, Fourier, rapid Hartley
transform, Hough transform, and Radon
transform are examples of mathematical
operations. Fourier Transform is the most
popular (FT).
 Image matching and different
approaches:
In machine vision and robotics,
feature detection and picture
matching are still significant topics
to concentrate on. With picture
changes such as rotation, scale,
lighting, noise, and affine
transformations, an ideal feature
identification approach should be
used.
Image matching in DIP is a complex
process and it uses several
algorithms. Given below are 3 most
popular approaches:
 Scale Invariant Feature Transform
(SIFT)
 Speed up Robust Feature (SURF)
 Binary Robust Independent
Elementary Features (BRIEF)
 Oriented FAST and Rotated
BRIEF ORB
Conclusion:-
Picture processing has become an
indispensable component of contemporary
engineering, with applications in biometry,
signature recognition, image enhancement,
quality control in different manufacturing
industries, and machine vision methods,
among others. AIP and DIP are the two
most common forms of image processing.
DIP offers its own set of benefits. Image
sharpening and restoration, character
recognition, signature or pattern
identification, target recognition, and
image enhancement are just a few of the
uses for DIP. Each application employs a

8. single or a mix of techniques such as
picture acquisition, segmentation,
transformation, and enhancement, among
others. For image matching and DIP,
several algorithms like as SIFT, SURF,
BRIEF, and ORB are used. As a result,
this study discusses different elements of
DIP and provides in-depth information on
the applications, methods, and algorithms
used.
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