SlideShare a Scribd company logo
Digital Image Processing &
Computer Graphics
Digital Image Processing
• Digital image processing is a branch of computer
which refers to processing digital images by
means of digital computer.
• An image is too dimensional function G(X,Y). Here
X,Y are the coordinate of plane.
• Gray level: With respect to two dimensional
function G(X,Y) the amplitude of G at any pair of
coordinate (x,y) is known as intensity or gray level
of image at that point.
Representation of Image
Here Number represents gray level of each pixel.
An image can be represented in two dimensional array.
122 200 240 123 245
76 84 234 122 120
110 90 190 170 150
235 255 243 214 223
69 74 234 233 214
Digital Image processing
• Geometric transformations: resizing (seam
Carving), Rotation, Shearing, Scaling etc.
• Image refinement (noise removal)
• Color adjustment: Contrast Stretching
• Compositing: combination of two or more images
• Many other operations
Difference between computer graphics
and digital image processing
• Computer Graphics: construction of images
• DIP: Manipulation of images
Question
• Calculate Digital negative for the following
image.
122 200 240 123 245
76 84 234 122 120
110 90 190 170 150
235 255 243 214 223
69 74 234 233 214
Contrast Stretching in image
• Contrast Stretching: Many time we obtain low
contrast images due to poor illumination.
• The main idea behind contrast stretching is to
increase the contrast of the image by making
dark portion darker and the brighter portion
brighter.
Contrast Stretching
Original Image Duplicate image after contrast
stretching
Elementary Image Processing
Techniques
• Digital image processing deals with manipulation of
digital images through a digital computer.
• DIP focuses on developing a computer programs (In
MATLAB) that is able to perform processing on an
image.
• The input of system is a digital image and the system
process that image using efficient algorithms, and
gives an image as an output.
• The most common example is Adobe Photoshop. It
is one of the widely used application for processing
digital images
Elementary Image Processing
Techniques
• Image analysis involves processing an image into fundamental
components in order to extract statistical data.
• Image analysis can include such tasks as finding shapes (Line
Detection), detecting edges, removing noise, counting objects,
and measuring region and image properties of an object.
• Image analysis is a broad term that covers a range of techniques
that generally fit into these subcategories:
• Image enhancement
• Image Restoration
• Image segmentation
• Image Resizing
• Image Compression
• Feature Extraction
Image Processing Techniques
Image Enhancement
• Image manipulation in digital image processing is possible
with the use of software. Image manipulation can involve.
• Manipulation of images to make an image lighter or darker
• To increase or decrease contrast (Contrast Stretching).
• Advanced image enhancement software
also supports many filters for altering images in various
ways. Programs specialized for image enhancements are
sometimes called image editors.
Image Processing Techniques Cont..
Image restoration
Noise Removal using various filters. Corruption may come in many forms such as motion
blur, noise and camera mis-focus.
Image restoration refers to removal or minimization of degradations in an image. This
includes de-blurring of images degraded by the limitations of a sensor or its
environment, noise filtering, and correction of geometric distortion or non-linearity due
to sensors.
Image Processing Techniques Cont..
Image Segmentation
• In computer vision (Computer vision is concerned with the
automatic extraction, analysis and understanding of useful
information from a single image or a sequence of images), image
segmentation is the process of partitioning a digital image into
multiple segments (sets of pixels, also known as super pixels).
• The goal of segmentation is to simplify and/or change the
representation of an image into something that is more meaningful
and easier to analyze. (Segmentation on the basis of color).
• Image segmentation is typically used to locate objects
and boundaries (lines, curves, etc.) in images. More precisely, image
segmentation is the process of assigning a label to every pixel in an
image such that pixels with the same label share certain
characteristics.
• The result of image segmentation is a set of segments that
collectively cover the entire image, or a set of contours extracted
from the image (see edge detection). Each of the pixels in a region
are similar with respect to some characteristic or computed
property, such as color, intensity, or texture.
Image Processing Techniques Cont..
Image Processing Techniques Cont..
Original Image Segmented Image
Image Processing Techniques Cont..
Image Compression
• Image compression is minimizing the size in bytes of a graphics file
without degrading the quality of the image to an unacceptable level.
• The reduction in file size allows more images to be stored in a given
amount of disk or memory space. It also reduces the time required for
images to be sent over the Internet or downloaded from Web pages.
• There are several different ways in which image files can be compressed.
For Internet use, the two most common compressed graphic image
formats are the JPEG format and the GIF format.
• Various techniques available for lossy and lossless compressions. One of
most popular compression techniques, JPEG (Joint Photographic Experts
Group) uses Discrete Cosine Transformation (DCT) based compression
technique.
• Currently wavelet based compression techniques are used for higher
compression ratios with minimal loss of data.
Image Processing Techniques Cont..
Image Resizing (Content Aware)
• Content aware image resizing comprises algorithm which
are used to resize image in content aware manner. Seam
carving is algorithm which is used to resize image with out
distortion of important objects which are highly noticeable
with human eye.
• In computer vision visual saliency detection comprises wide
range of methods to detect salient object present in the
image. These methods focus how important object can be
detect from the image. Results of these methods are fully
dependent upon the type and quality of input image.
Content Aware Image resizing involves saliency detection
methods, Fixation prediction models, saliency map
generated through various saliency detection algorithms
and its evaluation measures are analyzed.
Image Processing Techniques Cont..
Image Processing Techniques Cont..
Visual Saliency Detection after applying various visual saliency detection algorithm
Image Processing Techniques Cont..
Feature Extraction
• When the data is too large to be processed, the data will be transformed into a
reduced representation set of features. The process of transforming the input data
into the set of features is called feature extraction.
Read the target image containing a
cluttered scene.
Read the reference image containing the
object of interest.
Image Processing Techniques Cont..
Detect Feature Points
Image Processing Techniques Cont..
Image Processing Techniques Cont..
Image Processing Techniques Cont..
Image Retrieval System
• An image retrieval system is a computer system
for browsing, searching and retrieving images
from a large database of digital images.
• Content-based image retrieval (CBIR) is the
application of computer vision to the image
retrieval problem, that is, the problem of
searching for digital images in large databases.
• "Content-based" means that the search will
analyze the actual contents of the image. The
term 'content' in this context might refer colors,
shapes, textures, or any other information that
can be derived form the image itself.
Image Processing Techniques Cont..
How to search images?????
• Color
• Local Shape
• Texture
Color:
• Color similarity is achieved by computing a color histogram for
each image that identifies the proportion of pixels within an
image holding specific values (that humans express as colors).
• Examining images based on the colors they contain is one of
the most widely used techniques because it does not depend
on image size or orientation.
• Color searches will usually involve comparing color
histograms, though this is not the only technique in practice.
Color Histogram
• An image histogram is a type
of histogram that acts as a graphical
representation of the tonal distribution in a
digital image. It plots the number of pixels for
each tonal value. By looking at
the histogram for a specific image a viewer
will be able to judge the entire tonal
distribution at a glance.
Color Histogram
Shape:
• Shape does not refer to the shape of an image but to the
shape of a particular region that is being sought out.
• Shapes will often be determined first applying segmentation
or edge detection to an image.
• Other methods like use shape filters to identify given shapes
of an image.
Texture:
• Texture measures look for visual patterns in images and how they are spatially defined.
• These sets not only define the texture, but also where in the image the texture is
located.
• Texture is a difficult concept to represent. The identification of specific textures in an
image is achieved primarily by modeling texture as a two-dimensional gray level
variation.
Image Filtering Techniques
• The Purpose of filtering is to reduce noise and improve the visual quality of the image.
Noise types
• Gaussian Noise
• Gamma Noise
• Uniform noise
• Exponential Noise
Image Filtering Techniques Cont..
• The convolution process can be applied in
image filtering.
• By applying filters over the image we can
reduce or remove noise present in the image.
Filters which can applying over the image are
• High pass filter
• Band pass filter
• Low pass filter
Image Filtering Techniques Cont..
• In convolution process we multiply each
component of the mask with the
corresponding value of the image and add
them up and place the value that we get, at
the center.
(x-1,y+1) (x,y+1) (x+1,y+1)
(x-1,Y) (x, y) (x+1,y)
(x-1,y-1) (x,y-1) (x+1,y-1)
W1 W2 W3
W4 W5 W6
W7 W8 w9
Original Image Mask
Image Filtering Techniques Cont..
• Suppose f(x, y) is the modified image after convolution
process, then we have
F(x, y)=g(x-1,y+1)*W1+g(x,y+1)*W2+g(x+1,y+1)*W3+g(x-1,y)*W4+f(x, y)*W5+f(x+1,y)*W6+(x-1,y-1)*W7
+f(x,y-1)*W8+f(x+1,y-1)*W9
• The convolution process can be applied in image filtering . By
applying filters over the image we can reduce or remove
noise present in the image.
• Filters can be applied on low frequency region and high
frequency region of the image.
Example
0.5
0.5 00
0
0 00
mask
8
Modified image dataLocal image
neighborhood
6 14
1 81
5 310
1
Image Filtering Techniques Cont..
• Frequency in image means gray levels. An
image can have low frequency regions or high
frequency regions.
• Low frequency region: Region in image where
gray levels changes slowly over a region.
• High Frequency Region: Region in the image
where gray levels changes rapidly.
Image Filtering Techniques Cont..
• In most of the image background is
considered to be low frequency region
whereas edges are considered to be high
frequency regions.
• Low pass filters- Use to remove high
frequency in image(Edge Removal)
• High Pass filters- Remove low frequency in
image (Background)
Thank you!

More Related Content

PPTX
Image filtering in Digital image processing
PPTX
Cyrus beck line clipping algorithm
PPTX
Image Representation & Descriptors
PPTX
RECURSIVE DESCENT PARSING
PPTX
Chapter 3 image enhancement (spatial domain)
PDF
Digital Image Processing: Image Segmentation
PDF
Ordinal Regression and Machine Learning: Applications, Methods, Metrics
PPT
Fields of digital image processing slides
Image filtering in Digital image processing
Cyrus beck line clipping algorithm
Image Representation & Descriptors
RECURSIVE DESCENT PARSING
Chapter 3 image enhancement (spatial domain)
Digital Image Processing: Image Segmentation
Ordinal Regression and Machine Learning: Applications, Methods, Metrics
Fields of digital image processing slides

What's hot (20)

PPTX
BRESENHAM’S LINE DRAWING ALGORITHM
PPTX
Animation in Computer Graphics
PPTX
3D Transformation in Computer Graphics
PPTX
Computer graphics(parametric cubic curves)
PPT
Chapter10 image segmentation
PDF
Lecture 3 image sampling and quantization
PPT
ImageProcessing10-Segmentation(Thresholding) (1).ppt
PPTX
Alpha beta pruning in ai
PPSX
Image Processing: Spatial filters
PPTX
Introduction to Graph Theory
PPTX
Log Transformation in Image Processing with Example
PPTX
Polygon mesh
PPTX
Random forest
PPTX
Support Vector Machine ppt presentation
PPTX
Liang- Barsky Algorithm, Polygon clipping & pipeline clipping of polygons
PPTX
daa-unit-3-greedy method
PPTX
Computer Graphics
PPT
Image segmentation ppt
PPTX
Graphics pipelining
PPT
Hidden surfaces
BRESENHAM’S LINE DRAWING ALGORITHM
Animation in Computer Graphics
3D Transformation in Computer Graphics
Computer graphics(parametric cubic curves)
Chapter10 image segmentation
Lecture 3 image sampling and quantization
ImageProcessing10-Segmentation(Thresholding) (1).ppt
Alpha beta pruning in ai
Image Processing: Spatial filters
Introduction to Graph Theory
Log Transformation in Image Processing with Example
Polygon mesh
Random forest
Support Vector Machine ppt presentation
Liang- Barsky Algorithm, Polygon clipping & pipeline clipping of polygons
daa-unit-3-greedy method
Computer Graphics
Image segmentation ppt
Graphics pipelining
Hidden surfaces
Ad

Viewers also liked (8)

PPT
Line clipping
PPT
Polygon filling
PPT
Polygon clipping
PPT
Line drawing algorithm and antialiasing techniques
PPT
3 d transformations
PPT
Projection ppt
DOCX
Curve clipping
PPT
Character generation
Line clipping
Polygon filling
Polygon clipping
Line drawing algorithm and antialiasing techniques
3 d transformations
Projection ppt
Curve clipping
Character generation
Ad

Similar to Digital image processing & computer graphics (20)

PPT
Machine Vision lecture notes for Unit 3.ppt
PPTX
Introduction to Medical Imaging Applications
PPTX
Fundamental steps in Digital Image Processing.pptx
PPTX
AI Unit-5 Image Processing for all ML problems
PDF
Image processing.pdf
PPTX
image processing using matlab in faculty 1
PPTX
Digital image processing
PPT
Scct2013 topic 3_graphics
PPTX
Digital Image Processing Unit 2 ppt.pptx
PPTX
Image processing and compression.pptx
PPTX
Chapter-1 Digital Image Processing (DIP)
PPTX
Fundamental steps in image processing
PPT
Digital Image Processing
PPTX
Image processing and It’s forensic significance
PPTX
ACMP340.pptx
PPTX
DIGITAL IMAGE PROCESSING slides in pptx.
PPTX
Image & Graphics
PPT
Fundamentals of Image Processing & Components.ppt
PDF
A (very brief) Introduction to Image Processing and 3D Printing with ImageJ
PPTX
INTRODUCTION TO DIGITAL IMAGE PROCESSING.pptx
Machine Vision lecture notes for Unit 3.ppt
Introduction to Medical Imaging Applications
Fundamental steps in Digital Image Processing.pptx
AI Unit-5 Image Processing for all ML problems
Image processing.pdf
image processing using matlab in faculty 1
Digital image processing
Scct2013 topic 3_graphics
Digital Image Processing Unit 2 ppt.pptx
Image processing and compression.pptx
Chapter-1 Digital Image Processing (DIP)
Fundamental steps in image processing
Digital Image Processing
Image processing and It’s forensic significance
ACMP340.pptx
DIGITAL IMAGE PROCESSING slides in pptx.
Image & Graphics
Fundamentals of Image Processing & Components.ppt
A (very brief) Introduction to Image Processing and 3D Printing with ImageJ
INTRODUCTION TO DIGITAL IMAGE PROCESSING.pptx

More from Ankit Garg (12)

PPT
Introduction to computer graphics part 2
PPT
Introduction to computer graphics part 1
PPT
Window to viewport transformation
PPT
Unit 1
PPTX
Numerical unit 1
PPTX
Hidden surface removal
PPTX
Graphics software standards
PPTX
Fractal introduction and applications modified version
PPTX
Concept of basic illumination model
PPTX
Circle generation algorithm
PPT
Applications of cg
PPT
2 d transformation
Introduction to computer graphics part 2
Introduction to computer graphics part 1
Window to viewport transformation
Unit 1
Numerical unit 1
Hidden surface removal
Graphics software standards
Fractal introduction and applications modified version
Concept of basic illumination model
Circle generation algorithm
Applications of cg
2 d transformation

Recently uploaded (20)

DOCX
573137875-Attendance-Management-System-original
PPTX
Infosys Presentation by1.Riyan Bagwan 2.Samadhan Naiknavare 3.Gaurav Shinde 4...
PPTX
FINAL REVIEW FOR COPD DIANOSIS FOR PULMONARY DISEASE.pptx
PPTX
UNIT 4 Total Quality Management .pptx
PPTX
CH1 Production IntroductoryConcepts.pptx
PDF
Operating System & Kernel Study Guide-1 - converted.pdf
PDF
SM_6th-Sem__Cse_Internet-of-Things.pdf IOT
PDF
Embodied AI: Ushering in the Next Era of Intelligent Systems
PPTX
Current and future trends in Computer Vision.pptx
PDF
R24 SURVEYING LAB MANUAL for civil enggi
PPTX
web development for engineering and engineering
PDF
Evaluating the Democratization of the Turkish Armed Forces from a Normative P...
PPTX
Lecture Notes Electrical Wiring System Components
DOCX
ASol_English-Language-Literature-Set-1-27-02-2023-converted.docx
PDF
keyrequirementskkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
PPTX
OOP with Java - Java Introduction (Basics)
PPT
Introduction, IoT Design Methodology, Case Study on IoT System for Weather Mo...
PDF
Mohammad Mahdi Farshadian CV - Prospective PhD Student 2026
PDF
TFEC-4-2020-Design-Guide-for-Timber-Roof-Trusses.pdf
PPTX
Safety Seminar civil to be ensured for safe working.
573137875-Attendance-Management-System-original
Infosys Presentation by1.Riyan Bagwan 2.Samadhan Naiknavare 3.Gaurav Shinde 4...
FINAL REVIEW FOR COPD DIANOSIS FOR PULMONARY DISEASE.pptx
UNIT 4 Total Quality Management .pptx
CH1 Production IntroductoryConcepts.pptx
Operating System & Kernel Study Guide-1 - converted.pdf
SM_6th-Sem__Cse_Internet-of-Things.pdf IOT
Embodied AI: Ushering in the Next Era of Intelligent Systems
Current and future trends in Computer Vision.pptx
R24 SURVEYING LAB MANUAL for civil enggi
web development for engineering and engineering
Evaluating the Democratization of the Turkish Armed Forces from a Normative P...
Lecture Notes Electrical Wiring System Components
ASol_English-Language-Literature-Set-1-27-02-2023-converted.docx
keyrequirementskkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
OOP with Java - Java Introduction (Basics)
Introduction, IoT Design Methodology, Case Study on IoT System for Weather Mo...
Mohammad Mahdi Farshadian CV - Prospective PhD Student 2026
TFEC-4-2020-Design-Guide-for-Timber-Roof-Trusses.pdf
Safety Seminar civil to be ensured for safe working.

Digital image processing & computer graphics

  • 1. Digital Image Processing & Computer Graphics
  • 2. Digital Image Processing • Digital image processing is a branch of computer which refers to processing digital images by means of digital computer. • An image is too dimensional function G(X,Y). Here X,Y are the coordinate of plane. • Gray level: With respect to two dimensional function G(X,Y) the amplitude of G at any pair of coordinate (x,y) is known as intensity or gray level of image at that point.
  • 3. Representation of Image Here Number represents gray level of each pixel. An image can be represented in two dimensional array. 122 200 240 123 245 76 84 234 122 120 110 90 190 170 150 235 255 243 214 223 69 74 234 233 214
  • 4. Digital Image processing • Geometric transformations: resizing (seam Carving), Rotation, Shearing, Scaling etc. • Image refinement (noise removal) • Color adjustment: Contrast Stretching • Compositing: combination of two or more images • Many other operations
  • 5. Difference between computer graphics and digital image processing • Computer Graphics: construction of images • DIP: Manipulation of images
  • 6. Question • Calculate Digital negative for the following image. 122 200 240 123 245 76 84 234 122 120 110 90 190 170 150 235 255 243 214 223 69 74 234 233 214
  • 7. Contrast Stretching in image • Contrast Stretching: Many time we obtain low contrast images due to poor illumination. • The main idea behind contrast stretching is to increase the contrast of the image by making dark portion darker and the brighter portion brighter.
  • 8. Contrast Stretching Original Image Duplicate image after contrast stretching
  • 9. Elementary Image Processing Techniques • Digital image processing deals with manipulation of digital images through a digital computer. • DIP focuses on developing a computer programs (In MATLAB) that is able to perform processing on an image. • The input of system is a digital image and the system process that image using efficient algorithms, and gives an image as an output. • The most common example is Adobe Photoshop. It is one of the widely used application for processing digital images
  • 10. Elementary Image Processing Techniques • Image analysis involves processing an image into fundamental components in order to extract statistical data. • Image analysis can include such tasks as finding shapes (Line Detection), detecting edges, removing noise, counting objects, and measuring region and image properties of an object. • Image analysis is a broad term that covers a range of techniques that generally fit into these subcategories: • Image enhancement • Image Restoration • Image segmentation • Image Resizing • Image Compression • Feature Extraction
  • 11. Image Processing Techniques Image Enhancement • Image manipulation in digital image processing is possible with the use of software. Image manipulation can involve. • Manipulation of images to make an image lighter or darker • To increase or decrease contrast (Contrast Stretching). • Advanced image enhancement software also supports many filters for altering images in various ways. Programs specialized for image enhancements are sometimes called image editors.
  • 12. Image Processing Techniques Cont.. Image restoration Noise Removal using various filters. Corruption may come in many forms such as motion blur, noise and camera mis-focus. Image restoration refers to removal or minimization of degradations in an image. This includes de-blurring of images degraded by the limitations of a sensor or its environment, noise filtering, and correction of geometric distortion or non-linearity due to sensors.
  • 13. Image Processing Techniques Cont.. Image Segmentation • In computer vision (Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images), image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as super pixels). • The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. (Segmentation on the basis of color). • Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. • The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture.
  • 15. Image Processing Techniques Cont.. Original Image Segmented Image
  • 16. Image Processing Techniques Cont.. Image Compression • Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. • The reduction in file size allows more images to be stored in a given amount of disk or memory space. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages. • There are several different ways in which image files can be compressed. For Internet use, the two most common compressed graphic image formats are the JPEG format and the GIF format. • Various techniques available for lossy and lossless compressions. One of most popular compression techniques, JPEG (Joint Photographic Experts Group) uses Discrete Cosine Transformation (DCT) based compression technique. • Currently wavelet based compression techniques are used for higher compression ratios with minimal loss of data.
  • 17. Image Processing Techniques Cont.. Image Resizing (Content Aware) • Content aware image resizing comprises algorithm which are used to resize image in content aware manner. Seam carving is algorithm which is used to resize image with out distortion of important objects which are highly noticeable with human eye. • In computer vision visual saliency detection comprises wide range of methods to detect salient object present in the image. These methods focus how important object can be detect from the image. Results of these methods are fully dependent upon the type and quality of input image. Content Aware Image resizing involves saliency detection methods, Fixation prediction models, saliency map generated through various saliency detection algorithms and its evaluation measures are analyzed.
  • 19. Image Processing Techniques Cont.. Visual Saliency Detection after applying various visual saliency detection algorithm
  • 20. Image Processing Techniques Cont.. Feature Extraction • When the data is too large to be processed, the data will be transformed into a reduced representation set of features. The process of transforming the input data into the set of features is called feature extraction. Read the target image containing a cluttered scene. Read the reference image containing the object of interest.
  • 21. Image Processing Techniques Cont.. Detect Feature Points
  • 24. Image Processing Techniques Cont.. Image Retrieval System • An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. • Content-based image retrieval (CBIR) is the application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases. • "Content-based" means that the search will analyze the actual contents of the image. The term 'content' in this context might refer colors, shapes, textures, or any other information that can be derived form the image itself.
  • 26. How to search images????? • Color • Local Shape • Texture
  • 27. Color: • Color similarity is achieved by computing a color histogram for each image that identifies the proportion of pixels within an image holding specific values (that humans express as colors). • Examining images based on the colors they contain is one of the most widely used techniques because it does not depend on image size or orientation. • Color searches will usually involve comparing color histograms, though this is not the only technique in practice.
  • 28. Color Histogram • An image histogram is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image. It plots the number of pixels for each tonal value. By looking at the histogram for a specific image a viewer will be able to judge the entire tonal distribution at a glance.
  • 30. Shape: • Shape does not refer to the shape of an image but to the shape of a particular region that is being sought out. • Shapes will often be determined first applying segmentation or edge detection to an image. • Other methods like use shape filters to identify given shapes of an image.
  • 31. Texture: • Texture measures look for visual patterns in images and how they are spatially defined. • These sets not only define the texture, but also where in the image the texture is located. • Texture is a difficult concept to represent. The identification of specific textures in an image is achieved primarily by modeling texture as a two-dimensional gray level variation.
  • 32. Image Filtering Techniques • The Purpose of filtering is to reduce noise and improve the visual quality of the image. Noise types • Gaussian Noise • Gamma Noise • Uniform noise • Exponential Noise
  • 33. Image Filtering Techniques Cont.. • The convolution process can be applied in image filtering. • By applying filters over the image we can reduce or remove noise present in the image. Filters which can applying over the image are • High pass filter • Band pass filter • Low pass filter
  • 34. Image Filtering Techniques Cont.. • In convolution process we multiply each component of the mask with the corresponding value of the image and add them up and place the value that we get, at the center. (x-1,y+1) (x,y+1) (x+1,y+1) (x-1,Y) (x, y) (x+1,y) (x-1,y-1) (x,y-1) (x+1,y-1) W1 W2 W3 W4 W5 W6 W7 W8 w9 Original Image Mask
  • 35. Image Filtering Techniques Cont.. • Suppose f(x, y) is the modified image after convolution process, then we have F(x, y)=g(x-1,y+1)*W1+g(x,y+1)*W2+g(x+1,y+1)*W3+g(x-1,y)*W4+f(x, y)*W5+f(x+1,y)*W6+(x-1,y-1)*W7 +f(x,y-1)*W8+f(x+1,y-1)*W9 • The convolution process can be applied in image filtering . By applying filters over the image we can reduce or remove noise present in the image. • Filters can be applied on low frequency region and high frequency region of the image.
  • 36. Example 0.5 0.5 00 0 0 00 mask 8 Modified image dataLocal image neighborhood 6 14 1 81 5 310 1
  • 37. Image Filtering Techniques Cont.. • Frequency in image means gray levels. An image can have low frequency regions or high frequency regions. • Low frequency region: Region in image where gray levels changes slowly over a region. • High Frequency Region: Region in the image where gray levels changes rapidly.
  • 38. Image Filtering Techniques Cont.. • In most of the image background is considered to be low frequency region whereas edges are considered to be high frequency regions. • Low pass filters- Use to remove high frequency in image(Edge Removal) • High Pass filters- Remove low frequency in image (Background)