A Review on Overview of Image Processing Techniques

International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 4 Issue 5, August 2020 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD31819 | Volume – 4 | Issue – 5 | July-August 2020 Page 346
A Review on Overview of Image Processing Techniques
Hirdesh Chack1, Vijay Kumar Kalakar2, Syed Tariq Ali2
1,2Lecturer, Department of Electronics and Telecommunication,
1Government Polytechnic College, Jatara, Madhya Pradesh, India
2Government Women’s Polytechnic College, Bhopal, Madhya Pradesh, India
ABSTRACT
Image processing is actually among the fast-growing innovations across
various areas of a business with applications. Image processing frequently
forms key scientific areas within the areas of electronics and computer
science. Image processing is a tool for refining raw photographs obtained in
our everyday lives from rockets, ships, space samples ormilitaryidentification
flights. Thanks to technologically powerful personal computers, broad
databases of current devices and the Graphic Technology and the accessible
resources for such software and apps, this area is strong and common. The
provided input is an image and its output an enhanced high-quality image
according to the techniques used in the image processing procedure. Image
processing is typically called digital image processing, although it is often
possible to optically process and analogy photograph. An overview of image
processing methods is given in this article. This article focuses mainly on
identifying specific methods utilized in various image processing phases.
KEYWORDS: Image Processing, Image Processing Techniques, Segmentation,
Enhancement
How to cite this paper: Hirdesh Chack |
Vijay Kumar Kalakar | Syed Tariq Ali "A
Review on Overview of Image Processing
Techniques"
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of Trend in Scientific
Research and
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/4.0)
I. INTRODUCTION
Image Processing is a method for enhancement of raw
photographs from spacecraft, space probes and aircraft or
pictures obtained from cameras or sensors similar systems
provide regular existence. Over the last five decades, many
technologies have been established in the area of image
processing. Many techniques for enhancing pictures of
spacecraft’s, spatial samples and military inspection flights
have been developed. The simple availability of powerful
personal computers, large-size memorydevicesandgraphics
applications are making image processing systems more
common.
Image Processing are given two methods as follow:
 Analog Image Processing
 Digital Image Processing
Computer algorithmsare used in digitalimageprocessingfor
the rendering of images. Unlike analog image processing,
digital image processing provides a range ofadvantages.The
input data was used for a broad variety of algorithms. In
digital image processing, at any stage during signal
processing we can prevent such processing issues, such as
noise and signal distortion. Throughout the 2000s, fast
computers became a common method of image processing
for signal processing and digital image processing. Of this
purpose, the processing of signal images has been both
flexible and cheapest [1].
Figure 1 Digital signal processing of Image
For hardcopies suchas printoutsand photos, image processing utilizinganalog techniques can berequired.Imageanalystsuse
these graphic toolsacrossa number of basics of perception. The analysis of photographs is not only confinedtotheregiontobe
IJTSRD31819

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
learned, but also to expert knowledges. Association is another essential method of image analysis. Analysts use a mix of
professional and collateral information in the analysis of images.
Image processing has a strong connection to computer vision and graphics. The image processing priorities can be classified
into five groups:
 Hallucination (Monitoring events not visible).
 Restoring and sharpening images (for improved image creation).
 Image repossession (image of interest search).
 Pattern analysis (measures a representation of a variety of objects)
 Recognition of image (difference of artifacts in an image)
II. TRANSFORMATIONS IN IMAGE PROCESSING
1. Image-to-Image transformation
 Enhancement
 Restoration
 Geometry
2. Image to information transformation
 Image statistics (histograms) histogram helps in analyzing and processing the image
 Image compression
 Image analysis includes image segmentation, extracting the features in image, pattern recognition scheme)
 Computer-aided design.
3. Information-to image transformation
 Decompression from the image which is already compressed.
 Reconstruction of small parts of images to forms new original image.
 Animations Computer graphics, and virtual reality.
III. IMAGE PROCESSING TECHNIQUES
Digital image processing has developed various techniques in recent years. The accompanying diagram illustrates different
phases in the processing of images and the manner in which they are done. The reference image or the video frame isused for
all these measures.
Classification of Image Processing techniques are given below:-
1. Image representation
2. Image preprocessing
3. Image enhancement
4. Image analysis
5. Image compression
6. Image Segmentation
7. Image Restoration
3.1. Image representation
Representation involves the conversion of raw data to a type appropriate for more operation by computers. Two types of
representation techniques are:
 Representation of boundaries
 Representation of the region
If the emphasis is on internal shape characteristics such as corner, squared, border representation is sufficient.
Regional representation when the emphasis is on internal characteristics e is acceptable. e.g.. Skeleton, structure, shape.
Figure 2 2D Image Digital Representation

3.2. Image preprocessing
Preprocessing indicates that the same tissue type may have a different scale of signal intensities for different images.
Preprocessing functions involve those operations that arenormally required prior to the main data analysisand extraction of
information and are generally grouped as radiometric orgeometric corrections.Radiometriccorrectionsincludecorrectingthe
data for sensor irregularities and unwanted sensor or atmospheric noise, removal of non-brain vowelsandconvertingthedata
so they accurately represent the reflected or emitted radiation to find out a transformation between two images precisely.
The preprocessed imageswill have some noisewhich should be removed for the further processingoftheimage.Imagenoiseis
most apparent in image regions with low signal level such as shadow regions or under exposed images. There are so many
types of noise like salt and pepper noise, film grains.All these noise are removed by using algorithms.Amongtheseveralfilters,
median filter is used.
Image noise is more noticeable in low-signal environments, such as shadow zones or visible images. Too many kindsof noise
occur, suchas salt and pepper static, and movie grains. Theformulasare used to suppress all such sounds. The median filter is
used among the various filters.
3.3. Image enhancement
Image enhancement is the process bywhich the effects of the image can be rendered better, changed from the initialimagesso
that the effects become more appropriate for processing or further study of the image. It helps to remove noise, sharpen the
image or brighten the image, making it easy to identify key features. The process of improving the quality of the images from
the original image by removing noise, improves the imageby sharpening the original image and increasing theimagecontrast.
Figure 3 Enhanced Example Image
3.4. Image analysis
Image analysis approaches derive information from an image using automated or semi-automatic techniques such as scene
interpretation, image classification, image comprehension, object recognition, computer / machine vision. Image analysis
differs from other types of image processing techniques, such as improvement or reconstruction, in that theendproductofthe
process of image analysis is a numerical production rather than a video.
3.5. Image compression
Image compression minimizes the size of an image file bytes without reducing the consistency of the image order in order to
produce a finer image. The file size reduction allows more files to be stored in a given volume of disk or memory space. This
also eliminates the time it takes to transfer images over a network or import from a web page.
Two types of compression
1. Lossless
2. Lossy
Lossless Compression: In image compression, there is no loss in information regarding image, during compression of a text
file or program can be compressed without any errors and the application includes images stored in medical repository, text
file compression, and technical drawings.
 No loss of information
 Extracting original data from compressed image.
 Lower compression ratio
Lossy Compression: Compression techniques thatinvolves the loss of informationincluded in used at low bit rates,andused
in application streaming media and internet telephony.
 Loss of information.
 Perceptual loss of information reduced (controlled)
 Higher compression ratio

Figure 4 Lossless vs Lossy compression
3.6. Image Segmentation
It is the process ofbreakingdownan imageinto its constituent parts. Output is usually a raw pixel data. Image segmentationis
typically used to locate objects and boundaries (lines, curves, etc.)in images. More precisely,imagesegmentationistheprocess
of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.
Figure 5 Image Segmentation
Different methods of image segmentation:
 Region Based
 Edge Based
 Threshold
 Feature Based Clustering
Region Based
Region is a group of connected pixels having similar properties. Region based segmentation is a process of partitioning an
image into region. Regions are used to interpret images. Aregion may correspond to particular object or different parts of an
object. Region-based techniques are generally better in noisy images (where borders are difficult to detect). Fair accuracy
levels are offered in region based methods.
Edge Based
Image segmentation algorithms generally are based on discontinuous intensity values and similar intensity values. In case of
discontinuous intensity values, the approach is to partition the image based on abruptchanges in intensity, suchasedgesinan
image. Segmentation based on Edge Detection refers to the boundaries where there is an abrupt change in the intensity or
brightness value of the image. Edge detection is the problem of primary value inimageanalysis. The obtainedboundarymarks
the edges of the desired object. Hence by the detection of its edges, the object can be segmented from the image. The output
that is received by applying edge detection algorithm is a binary image. Edge based methods are interactive in nature. There
are three fundamental steps in edge detection:-

 Filtering & Enhancement: In order to facilitate the detection of edges, it is essential to repress as much noise as possible
and determine changes in intensity in the neighborhood of a point, without destroying the true edges.
 Detection of edge points: determine which edge pixels should be discarded as noise andwhich shouldberetained(usually,
thresholding provides the criterion used for detection).
 Edge localization: Not all of the points in an image are edges for a particular application. Edge localization determine the
exact location of an edge. Edge thinning and linking are usually required in this step
Threshold
Image segmentation by thresholding is a simple and powerful technique for segmenting images having light objectson shady
background. Thresholding operationconverts a multi-level imageinto a binary image by choosingan appropriate thresholdT
and divide image pixels into several regions and separate objects from background. The separation of the objects from the
background is generally done by selecting a value T. Depending on the thresholding value there are two techniques. Local
thresholding and global thresholding. When Tisconstant, the approachis called global thresholding otherwiseitiscalledlocal
thresholding. If the background illumination is uneven then the global thresholding method become failed. But these uneven
illuminations are compensated in local thresholding method by using multiple thresholds.
Feature Based Clustering
Clustering is the process ofgrouping together of objects based on some similar properties so that eachcluster containssimilar
objects which are dissimilar to the objects of other clusters. Clustering is a process which can be performed by different
algorithms using different methods for computingor finding the cluster. The quality of the good clusteringmethods produces
high intra-cluster and low inter-cluster similarities.A general approach to imageclustering involves addressing the following
issues:
1. How to represent the image.
2. How to organize the data.
3. How to classify an image to a certain cluster.
The Clustering methods are classified into K mean clustering, Fuzzy C- Means [FCM] Algorithm etc. Kmeans is one of the fast,
robust, simplest unsupervised learning algorithmsthat solve the well-known clustering problem. The methodistoclassifythe
given data set through a certain number of k clusters thatare fixed a priori. K-meansclustering algorithms givesoptimalresult
when data set are dissimilar. Fuzzy Clustering is a method which allow the objects to belong to more than one cluster with
different membership. This is the one of the effective method for pattern recognition. Most commonly used fuzzy clustering
algorithms is the Fuzzy C-Mean. By using FCM we can retain information of the data set. In FCM, the data point is assigned
membership to each cluster center as a result of which data point may belong to more than one cluster center.
3.7. Image Restoration
Restoring the clear image from the degraded or corrupted image is provided by the technique called image restoration.
Corrupted/Blur images are due to noisy, blur images or camera miscues. Blurring occurs due to formation of bandwidth
reduction of an ideal imagecaused byimperfectimage formation process. Thus the images will berestoredintooriginalquality
by reducing the physical degradation.
Degradation model
Distortion is due the imperfection in the imaging system that occurs mainly involved in stored images. This problem leads to
severe due to random noise involved in the imaging system. Degradation operation works on input image f(x, y) to lessen a
degraded image g(x, y).
Categories in image restoration technique Image restoration technique is classified into two types depending upon the
degradation of the image. If information about degradation is known previously, then deterministic method of image
restoration can be used. If it is not known then the stochastic method of image restoration has been introduced.
Figure 6 Image Restoration technique

IV. CONCLUSIONS
This paper addresses other methods for image processing,
such as segmentation, compression, edge detection, etc.
Choosing the type of image processing relies on the purpose
for whichit is to be used. Each procedure has its own benefit
and downside, but transforms the input image into the form
that is appropriate for further processing. This paper will
allow individuals to grasp the fundamental principles of
image processing.
In this article, we have presented a detailed analysis of the
image processing and its applications. We tried to show the
fundamentals of image analysis and segmentation
techniques. They addressed the fundamentals of image
processing, suchas image interpretation andunderstanding,
image manipulation, compression methods and their
applications. The segmentation approach can be classified
into different categories depending on the constraintchosen
for segmentation, such as pixel size, homogeneity,
discontinuity, cluster data, topology, etc. Any solution has
pros and cons. The result obtained using a single
segmentation approach cannot be the same as the other
approach.
Despite several decades of work, there is no widely adopted
image segmentationalgorithmbecause image segmentation
is influenced by several variables such as image size, color,
strength, noise level, etc. There is therefore no standard
algorithm applicable to all types of images and the
complexity of the problem. Because of both of the
aforementioned reasons, image segmentation remains a
significant unresolved concern in the field of image
processing. Techniques that are unique of particular
purposes also yield greater efficiency andchoosing the right
solution to the problem of segmentationcan beachallenging
task. A single solution to the section with all images can be
virtually impossible.
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