HIGH-FIDELITY DATA EMBEDDING FOR IMAGE ANNOTATION
HIGH-FIDELITY DATA EMBEDDING FOR IMAGE ANNOTATIONAbstract
In this particular project we propose a novel method for
embedding data in an image. High fidelity is a demanding
requirement for data hiding, especially for images with artistic or
medical value. This correspondence proposes a high-fidelity image
watermarking for annotation with robustness to moderate distortion.
To achieve the high fidelity of the embedded image, we introduce a
visual perception model that aims at quantifying the local
tolerance to noise for arbitrary imagery. Based on this model, we
embed two kinds of watermarks: a pilot watermark that indicates the
existence of the watermark and an information watermark that
conveys a payload of several dozen bits. The objective is to embed
32 bits of metadata into a single image in such a way that it is
robust to JPEG compression and cropping. We demonstrate the
effectiveness of the visual model and the application of the
proposed annotation technology using a database of challenging
photographs and medical images that contain a large amount of
smooth regions. The embedding and the performance are all carried
out in MATLAB simulation. CHAPTER I
INTRODUCTION
The term digital image refers to processing of a two dimensional
picture by a digital computer. In a broader context, it implies
digital processing of any two dimensional data. A digital image is
an array of real or complex numbers represented by a finite number
of bits. An image given in the form of a transparency, slide,
photograph or an X-ray is first digitized and stored as a matrix of
binary digits in computer memory. This digitized image can then be
processed and/or displayed on a high-resolution television monitor.
For display, the image is stored in a rapid-access buffer memory,
which refreshes the monitor at a rate of 25 frames per second to
produce a visually continuous display.
1.1 THE IMAGE PROCESSING SYSTEM
A typical digital image processing system is given in
fig.1.1
1.1.1 DIGITIZER
A digitizer converts an image into a numerical representation
suitable for input into a digital computer. Some common digitizers
are
1. Microdensitometer
2. Flying spot scanner
3. Image dissector
4. Videocon camera
5. Photosensitive solid- state arrays.
1.1.2 IMAGE PROCESSOR
An image processor does the functions of image acquisition,
storage, preprocessing, segmentation, representation, recognition
and interpretation and finally displays or records the resulting
image. The following block diagram gives the fundamental sequence
involved in an image processing system
.
As detailed in the diagram, the first step in the process is
image acquisition by an imaging sensor in conjunction with a
digitizer to digitize the image. The next step is the preprocessing
step where the image is improved being fed as an input to the other
processes. Preprocessing typically deals with enhancing, removing
noise, isolating regions, etc. Segmentation partitions an image
into its constituent parts or objects. The output of segmentation
is usually raw pixel data, which consists of either the boundary of
the region or the pixels in the region themselves. Representation
is the process of transforming the raw pixel data into a form
useful for subsequent processing by the computer. Description deals
with extracting features that are basic in differentiating one
class of objects from another. Recognition assigns a label to an
object based on the information provided by its descriptors.
Interpretation involves assigning meaning to an ensemble of
recognized objects. The knowledge about a problem domain is
incorporated into the knowledge base. The knowledge base guides the
operation of each processing module and also controls the
interaction between the modules. Not all modules need be
necessarily present for a specific function. The composition of the
image processing system depends on its application. The frame rate
of the image processor is normally around 25 frames per second.
1.1.3 DIGITAL COMPUTER
Mathematical processing of the digitized image such as
convolution, averaging, addition, subtraction, etc. are done by the
computer.
1.1.4 MASS STORAGE
The secondary storage devices normally used are floppy disks, CD
ROMs etc.
1.1.5 HARD COPY DEVICE
The hard copy device is used to produce a permanent copy of the
image and for the storage of the software involved.
1.1.6 OPERATOR CONSOLE
The operator console consists of equipment and arrangements for
verification of intermediate results and for alterations in the
software as and when require. The operator is also capable of
checking for any resulting errors and for the entry of requisite
data.
CHAPTER II
IMAGE PROCESSING FUNDAMENTALS
2.1 INTRODUCTION
Digital image processing refers processing of the image in
digital form. Modern cameras may directly take the image in digital
form but generally images are originated in optical form. They are
captured by video cameras and digitalized. The digitalization
process includes sampling, quantization. Then these images are
processed by the five fundamental processes, at least any one of
them, not necessarily all of them.
2.2 IMAGE PROCESSING TECHNIQUESThis section gives various image
processing techniques.
Fig 2.2.1 Image processing Techniques2.2.1 IMAGE ENHANCEMENT
Image enhancement operations improve the qualities of an image
like improving the images contrast and brightness characteristics,
reducing its noise content, or sharpen the details. This just
enhances the image and reveals the same information in more
understandable image. It does not add any information to it.
2.2.2 IMAGE RESTORATION
Image restoration like enhancement improves the qualities of
image but all the operations are mainly based on known, measured,
or degradations of the original image. Image restorations are used
to restore images with problems such as geometric distortion,
improper focus, repetitive noise, and camera motion. It is used to
correct images for known degradations.
2.2.3 IMAGE ANALYSIS
Image analysis operations produce numerical or graphical
information based on characteristics of the original image. They
break into objects and then classify them. They depend on the image
statistics. Common operations are extraction and description of
scene and image features, automated measurements, and object
classification. Image analyze are mainly used in machine vision
applications.
2.2.4 IMAGE COMPRESSION
Image compression and decompression reduce the data content
necessary to describe the image. Most of the images contain lot of
redundant information, compression removes all the redundancies.
Because of the compression the size is reduced, so efficiently
stored or transported. The compressed image is decompressed when
displayed. Lossless compression preserves the exact data in the
original image, but Lossy compression does not represent the
original image but provide excellent compression.
2.2.5 IMAGE SYNTHESIS
Image synthesis operations create images from other images or
non-image data. Image synthesis operations generally create images
that are either physically impossible or impractical to
acquire.
2.3 APPLICATIONS OF DIGITAL IMAGE PROCESSING
Digital image processing has a broad spectrum of applications,
such as remote sensing via satellites and other spacecrafts, image
transmission and storage for business applications, medical
processing, radar, sonar and acoustic image processing, robotics
and automated inspection of industrial parts.
2.3.1 MEDICAL APPLICATIONS
In medical applications, one is concerned with processing of
chest X-rays, cineangiograms, projection images of transaxial
tomography and other medical images that occur in radiology,
nuclear magnetic resonance (NMR) and ultrasonic scanning. These
images may be used for patient screening and monitoring or for
detection of tumours or other disease in patients.2.3.2 SATELLITE
IMAGING
Images acquired by satellites are useful in tracking of earth
resources; geographical mapping; prediction of agricultural crops,
urban growth and weather; flood and fire control; and many other
environmental applications. Space image applications include
recognition and analysis of objects contained in image obtained
from deep space-probe missions.
2.3.3 COMMUNICATION
Image transmission and storage applications occur in broadcast
television, teleconferencing, and transmission of facsimile images
for office automation, communication of computer networks,
closed-circuit television based security monitoring systems and in
military communications.
2.3.4 RADAR IMAGING SYSTEMS
Radar and sonar images are used for detection and recognition of
various types of targets or in guidance and manoeuvring of aircraft
or missile systems.2.3.5 DOCUMENT PROCESSING
It is used in scanning, and transmission for converting paper
documents to a digital image form, compressing the image, and
storing it on magnetic tape. It is also used in document reading
for automatically detecting and recognizing printed
characteristics.
2.3.6 DEFENSE/INTELLIGENCE
It is used in reconnaissance photo-interpretation for automatic
interpretation of earth satellite imagery to look for sensitive
targets or military threats and target acquisition and guidance for
recognizing and tracking targets in real-time smart-bomb and
missile-guidance systems.
BLOCK DIAGRAM
EXISTING SYSTEMS
Simplified frequency scaling model in their spread-spectrum
watermarking called as Realizing upper-resolution with superimposed
projection. More explicit masking models, including frequency
masking and spatial masking discussed in, On the resolution limits
of superimposed projection.
DISADVANTAGES There are limits due to the infinite signal ranges
of the sub-frames in practical cases. The reconstruction filter and
the sampling may independently cause the signal limits to be
exceeded, making it practically impossible to perfectly reproduce
all input signal frequencies and amplitudes.
PROPOSED METHODThis correspondence proposes a high-fidelity
image watermarking for annotation with robustness to moderate
distortion. To achieve the high fidelity of the embedded image, we
introduce a visual perception model that aims at quantifying the
local tolerance to noise for arbitrary imagery. Based on this
model, we embed two kinds of watermarks: a pilot watermark that
indicates the existence of the watermark and an information
watermark that conveys a payload of several dozen bits. The
objective is to embed 32 bits of metadata into a single image in
such a way that it is robust to JPEG compression and cropping.
ADVANTAGES
The two outputs are mixed using a nonlinear function and a
smoothing low-pass filter in a post-processing step. As a result,
image local region with sharp transitions as well as uniformly or
smoothly colored areas are distinguished as highly sensitive to
noise, whereas areas with random texture are identified as tolerant
to noise. The results on a database of highly challenging
photographic images and medical images show the effectiveness of
the proposed annotation technology.DOMAIN: DIGITAL IMAGE
PROCESSINGDigital Image processing is the use of computer
algorithms to perform image processing on Digital Images. As a
subfield of digital signal processing, digital image processing has
many advantages over analog image processing; it allows a much
wider range of algorithms to be applied to the input data, and can
avoid problems such as the build-up of noise and signal distortion
during processing.SOFTWARE REQUIREMENT
MATLAB 7.0 and above
MATLAB is a high-performance language for technical computing.
It integrates computation, visualization, and programming in an
easy-to-use environment where problems and solutions are expressed
in familiar mathematical notation. Typical uses include:
Math and computation
Algorithm development
Modeling, simulation, and prototyping
Data analysis, exploration, and visualization
Scientific and engineering graphics
Application development, including Graphical User Interface
building
MATLAB is an interactive system whose basic data element is an
array that does not require dimensioning. This allows you to solve
many technical computing problems, especially those with matrix and
vector formulations, in a fraction of the time it would take to
write a program in a scalar non-interactive language such as C or
FORTRAN.INTRODUCTION:
Data embedding provides a means to seamlessly associate
annotation data with host images. One of the key requirements of
data embedding based annotation is high fidelity, in order to
preserve the artistic or medical value of host images. A proper
visual model is important to control the distortion introduced by
embedded information. There have been a number of human visual
models proposed and employed in the watermarking literature which
employs a simplified frequency scaling model in their
spread-spectrum watermarking paper. More explicit masking models,
including frequency masking and spatial masking were developed.
Based on the works this project proposed a refined visual model to
reduce ringing artifacts along edges. The stringent requirement in
high-fidelity applications calls for more research into further
reducing perceptual artifacts in data embedding process. To achieve
such a goal, it is to introduce a pixel domain visual model which
combines the outputs of two filters: an entropy filter and a
differential standard deviation filter to estimate visual
sensitivity to noise. The two outputs are mixed using a nonlinear
function and a smoothing low-pass filter in a post processing step.
As a result, image local region with sharp transitions as well as
uniformly or smoothly colored areas are distinguished as highly
sensitive to noise, whereas areas with random texture are
identified as tolerant to noise. Based on the developed visual
model, we design a data embedding system that annotates images with
32 bits of metadata in a way that is robust to JPEG compression and
cropping. The 32-bit metadata is sufficient to represent the
indices to a database of around 4 billion images. To meet these
common requirements in image annotation applications the proposed
system embeds two watermarks: a pilot watermark and an information
watermark. The pilot watermark is a strong direct- sequence
spread-spectrum (SS) watermark to signal the existence of the
metadata and it is tiled over the image in the lapped biorthogonal
transform (LBT) domain. On top of the pilot watermark, we embed the
information watermark denoting the metadata bits using a regional
statistical quantization method. The quantization noise is
optimized to ensure the detection of both pilot and information
watermarks, as well as obey the constraints imposed by the
perceptual model. The detection is performed blindly without the
information from the host signal.SCOPE OF THE PROJECT:
The main objective is to propose a high-fidelity image
watermarking for annotation with robustness to moderate distortion.
To achieve this, a visual perception model is introduced to
quantify the localized tolerance to noise for arbitrary imagery.
The model is built by mixing the outputs from an entropy filter and
a differential localized standard deviation filter. It can
achieve
high fidelity for natural images with PSNR around 50 dB with
robustness to moderate JPEG compression, and meet the requirements
of near-lossless and almost-lossless for medical images with
robustness to moderate JPEG compression.STEGANOGRAPHY:
Steganographyis the art and science of writing hidden messages
in such a way that no one, apart from the sender and intended
recipient, suspects the existence of the message, a form ofsecurity
through obscurity. The wordsteganographyis ofGreekorigin and
means"concealed writing"from the Greek words steganos meaning
covered or protected, and graphein () meaning to write. The first
recorded use of the term was in 1499 byJohannes Trithemiusin
hisSteganographia, a treatise on cryptography and steganography
disguised as a book on magic. Generally, messages will appear to be
something else: images, articles, shopping lists, or some
othercovertextand, classically, the hidden message may be
ininvisible inkbetween the visible lines of a private letter.
The advantage of steganography, overcryptographyalone, is that
messages do not attract attention to themselves. Plainly visible
encrypted messagesno matter how unbreakablewill arouse suspicion,
and may in themselves be incriminating in countries
whereencryptionis illegal.Therefore, whereas cryptography protects
the contents of a message, steganography can be said to protect
both messages and communicating parties.
Steganography includes the concealment of information within
computer files. In digital steganography, electronic communications
may include steganographic coding inside of a transport layer, such
as a document file, image file, program or protocol. Media files
are ideal for steganographic transmission because of their large
size. As a simple example, a sender might start with an innocuous
image file and adjust the color of every 100th pixel to correspond
to a letter in the alphabet, a change so subtle that someone not
specifically looking for it is unlikely to notice it.DIGITAL
WATERMARKING:Digital watermarkingis the process of embedding
information into a digital signal in a way that is difficult to
remove. The signal may be audio, pictures or video, for example. If
the signal is copied, then the information is also carried in the
copy. A signal may carry several different watermarks at the same
time.
In visible watermarking, the information is visible in the
picture or video. Typically, the information is text or a logo
which identifies the owner of the media. The image on the right has
a visible watermark. When a television broadcaster adds its logo to
the corner of transmitted video, this is also a visible
watermark.
In invisible watermarking, information is added as digital data
to audio, picture or video, but it cannot be perceived as such
(although it may be possible todetectthat some amount of
information is hidden). The watermark may be intended for
widespread use and is thus made easy to retrieve or it may be a
form ofSteganography, where a party communicates a secret message
embedded in the digital signal. In either case, as in visible
watermarking, the objective is to attach ownership or other
descriptive information to the signal in a way that is difficult to
remove. It is also possible to use hidden embedded information as a
means of covert communication between individuals.
One application of watermarking is in copyright protection
systems, which are intended to prevent or deter unauthorized
copying of digital media. In this use a copy device retrieves the
watermark from the signal before making a copy; the device makes a
decision to copy or not depending on the contents of the watermark.
Another application is in source tracing. A watermark is embedded
into a digital signal at each point of distribution. If a copy of
the work is found later, then the watermark can be retrieved from
the copy and the source of the distribution is known. This
technique has been reportedly used to detect the source of
illegally copied movies.
Annotation of digital photographs with descriptive information
is another application of invisible watermarking.
While some file formats for digital media can contain additional
information calledmetadata, digital watermarking is distinct in
that the data is carried in the signal itself.The use of the word
of watermarking is derived from the much older notion of placing a
visiblewatermarkon paper.
Digital Watermarking can be used for a wide range of
applications such as:
Copyright protection
Source Tracking (Different recipients get differently
watermarked content)
Broadcast Monitoring (Television news often contains watermarked
video from international agencies)
Covert CommunicationIMAGE ANNOTATION:
Automatic image annotation(also known as automatic image
tagging) is the process by which a computer system automatically
assignsmetadatain the form of captioningorkeywordsto a digital
image. This application ofcomputer visiontechniques is used inimage
retrievalsystems to organize and locate images of interest from
adatabase.
This method can be regarded as a type
ofmulti-classimageclassificationwith a very large number of classes
- as large as the vocabulary size. Typically,image analysisin the
form of extracted feature vectors and the training annotation words
are used bymachine learningtechniques to attempt to automatically
apply annotations to new images. The first methods learned the
correlations between image features and training annotations, then
techniques were developed using machine translation to try and
translate the textual vocabulary with the 'visual vocabulary', or
clustered regions known asblobs. Work following these efforts have
included classification approaches, relevance models and so on.
The advantages of automatic image annotation versuscontent-based
image retrievalare that queries can be more naturally specified by
the user. CBIR generally (at present) requires users to search by
image concepts such as color and texture, or finding example
queries. Certain image features in example images may override the
concept that the user is really focusing on. The traditional
methods of image retrieval such as those used by libraries have
relied on manually annotated images, which is expensive and
time-consuming, especially given the large and constantly-growing
image databases in existence.
MODULE SEPERATION:
MODULE 1: Introduction of pixel domain visual model
MODULE 2: Design of data embedded system
MODULE 3: Watermark detection
MODULE I:
Introduction of pixel domain visual model
1.1 Design of Noise Tolerance Model
The proposed visual perceptual model is evaluated in the pixel
luminance domain, and we use the combination of local statistics
through two filters to quantify the noise tolerance of each pixel.
One filter estimates the entropy of a local region centered at the
pixel-of-interest, and the other computes the differential standard
deviation.
The output of the entropy filter indicates the content
complexity of the neighborhood for a given pixel k and we call it
entropy map E (k). The entropy map identifies pixels that are
perceptually less tolerant to noise, and usually works well for
pixels with low entropy, i.e., regions with smoothly changing
luminance. It is important to note that high value of entropy does
not necessarily imply strong tolerance to noise. Examples that do
not conform to this rule are irregular edges such as the one
illustrated in block Diagram
The differential standard deviation filter is used to detect the
effect of edges on visual fidelity. The differential standard
deviation is calculated as the difference of two standard
deviations centered at pixel k: D(k)=|S(k,r1)-S(k,r2)| with block
size r1>r2. If both S(k,r1) and S(k,r2) are low, then the
r1-neighborhood centered around k is considered not tolerant to
noise similarly to the entropy filter. On the other hand, if both
S(k,r1) and S(k,r2) have high values, it is very likely that the
visual content around is noisy and that it is more noise tolerant.
The interesting case occurs for disproportionate S(k,r1) and
S(k,r2); in most cases, this signals an edge in the neighborhood of
k and, thus, low tolerance to noise.We then combine the output of
the two filters by employing a mixing function that resembles a
smooth AND operator between E(k) and D(k) to arrive at the combined
signal of m(D,E). We note that, in m(D,E), pixels around sharp
edges have high values indicating high noise-tolerance. However,
experiments show that changes made near sharp edge areas are easily
visible and we should avoid making changes around those areas. To
achieve this, we generate a scaling map m(D,E) which has high
values at sharp edge areas and medium to low values at texture and
smooth regions to scale down the value of m(D,E) at edge areas. The
final output, called complexity map, is generated as
f(I)=m(D,E)/m(D,E). As a result, image local region with sharp
transitions as well as uniformly or smoothly colored areas are
distinguished as highly sensitive to noise, whereas areas with
random texture are identified as tolerant to noise.
In this correspondence, we implement the proposed human visual
model via the following filters. Given an image I, for each of its
pixels k(I), we examine its r by r neighborhood centered at k and
define the metric of entropy map E(k,r) as follows: (1)The standard
deviation S(k,r) is defined as
(2)The employed mixing function is nonlinear and has the shape
of a 2-D Gaussian distribution ..(3)Where D and E are normalized to
be within the range [0,1] and parameter s adjusts the shape of the
function. Low values of s raise the complexity value for the pixel
with both high E and D while suppressing other pixels. A large s
allows pixels with moderate E and D to have moderately high
complexity value. We generate the scaling map m(D,E) by applying a
3-by-3 spike filter on D(k) followed by a low-pass filter. The
spike filter is defined as F1={-1/8, -1/8, -1/8; -1/8, 1, -1/8;
-1/8, -1/8, -1/8}, with 1 in the center and -1/8 in the remaining
places. The final complexity map becomes f(I)=m(D,E)/m(D,E).The
objective of the data embedding is to add 32 bits of metadata into
an image so that their detection is as robust as possible to JPEG
compression and cropping. We apply the developed perceptual model
and build the system in two steps. We first embed a spread-spectrum
pilot watermark whose objective is to signal metadata presence. The
second layer of watermark would be the information watermark that
encodes the metadata.Pilot WatermarkThe pilot watermark (PW) serves
two purposes: to detect the existence of the metadata, and to
enable image registration at the detector side due to potential
cropping or other type of misalignment. We design the pilot
watermark to be a random ____ sequence taking values from ___ __ or
drawn from a standard normal distribution ___ __. The PW is spread
over a continuous image region of size _ _ __. We refer to this
region as the basic PW block. We create a full image watermark by
tiling the same basic PW block. Consequently, the smallest image
that can be ugmented with metadata is the size of the basic PW
block. In order to reduce blocking effects, we embed the PW in the
Lapped Biorthogonal Transform (LBT) domain [6]. LBT reduces the
blocking effect by synthesizing a block overlapped with its
neighboring blocks. The synthesis function decays to zero at the
block boundaries to facilitate the elimination of blocking effects.
In the LBT domain, we choose middle frequency components for
embedding in order to preserve high visual quality and ensure
robustness to lossy compression.We introduce a mask for each 4-by-4
block of the LBT image: _____ ____________ _ ,
where ones indicate the LBT coefficients used for PW
embedding.
Next, we use _ to adjust the energy of the PW according to the
content of the image. Since the complexity map is in the pixel
domain, we take the inverse LBT transform of the watermarked image
and re-weight the watermark in the pixel domain using _. With the
above considerations, the PW embedding can be expressed as _ _ _ __
___ __ ____ _ __ , where _ denotes the masked PW, and _ represents
the watermarked image. Parameter _ adjusts the watermark energy to
achieve the desired trade-off between visual quality and
robustness. In our experiments, we have used
_ __ _ in order to create images with 5055 dB PSNR with respect
to the original. The PW alone is hardly visible at these noise
levels.Information WatermarkOnce the pilot watermark is added, we
perform the embedding of the second watermark layer, the
information watermark (IW), which represents the metadata bits.
Since the detector has no access to the original image, the host
signal acts as a strong source of noise during detection. To reduce
the interference from the host signal, we perform quantization on
the first-order statistics of the source to embed the IW.
Specifically,we take the image region where one basicPWblock is
embedded and partition it into small building blocks. If the size
of the basic PW block is _ _ __, the number of pixels included in
each building block equals _ _ _ _ _____, where _ denotes the
scaling constant. Typically, we choose _ such that the size of the
building block is between 4_4 and 8_8 pixels. For example, for a
basic PW block of size 640_480, and _ ___ , we obtain _ _ _ ,
corresponding to a building block of dimensions 8_6 pixels. Then,
we randomly assign exactly_ distinct building blocks for each
metadata bit.We denote the set of all pixels that belong to these
building blocks as ___ ___ ___ .
We compute the first-order statistic of the pixel values in each
__ as
Watermark Detection:The detection process consists of two steps.
First, we determine whether the test image contains a PW. If yes,
we move on to extract the embedded metadata.To detect a pilot
watermark, we first transform the test image into the LBT domain to
get _ . Because the received image may have been cropped, we first
align the test image by detecting the PWs. This is done by sliding
the basic PW block_ over _ and examining the normalized
cross-correlation (NCC) values.
CONCLUSION:
In this correspondence, we propose a high-fidelity image
watermarking for annotation with robustness to moderate distortion.
To achieve the high fidelity of the embedded image, a visual
perception model is introduced to quantify the localized tolerance
to noise for arbitrary imagery. The model is built by mixing the
outputs from an
entropy filter and a differential localized standard deviation
filter. We then employ the proposed visual model to embed 32 bits
of metadata into a single image in a way that is robust to JPEG
compression and cropping while maintaining high fidelity. The
results on a database of highly challenging photographic images and
medical images show the
effectiveness of the proposed annotation technology. It can
achieve high fidelity for natural images with PSNR around 50 dB
with robustness to moderate JPEG compression, and meet the
requirements of near-lossless and almost-lossless for medical
images with robustness to moderate JPEG compression.
APPENDIX I
OUTPUTS
Input Image
Multiplier
STD Filter (3*3)
STD Filter (5*5)
Entropy Filter
Subtractor
Scale and Normalize
Spike Filter
Low-Pass Filter
Complexity Map
Digitizer
Mass Storage
Hard Copy Device
Display
Image Processor
Digital Computer
Operator Console
Fig 1.1 Block Diagram of a Typical Image Processing System
Problem Domain
Knowledge
Base
Segmentation
Preprocessing
Image Acquisition
Recognition & interpretation
Representation & Description
Result
Fig 1.2 Block Diagram of Fundamental Sequence involved in an
image Processing system
Image Enhancement
Image Restoration
Image Analysis
IP
Image Compression
Image Synthesis
