Digital Image Fundamentals
|
|
- Mark Kelley
- 5 years ago
- Views:
Transcription
1 Digital Image Fundamentals
2 Image Quality Objective/ subjective Machine/human beings Mathematical and Probabilistic/ human intuition and perception 6
3 Structure of the Human Eye photoreceptor cells 75~50 million Rod cell 6~7 million Cone cell 7
4 Human Visual Perception Perceived brightness is NOT a simple function of intensity Perceived brightness Actual illumination 8
5 Human Visual Perception Optical Illusion 9
6 Image Sensing and Acquisition Illumination Source EM energy, ultrasound, synthesized, Scene Element Objects, human organs, buried mineral, Sensing Material Single sensor: photodiode Sensor strips: require extensive processing Sensor arrays: CCD & CMOS
7 Image Sensing and Acquisition Single sensor Sensor Strip Circular Sensor Strip 2
8 Image Sensing and Acquisition Illumination source Image sampling & quantization Scene element Imaging system (Internal) image plane Output (digitized) image pixel (pel, picture element, image element) 3
9 Image Sampling & Quantization f ( x, y) A/D F( j, sampling quantization Original image ( x, y) f sampling quantization F( j, coordinates amplitude 5
10 Image Sampling & Quantization j k M-,,,0,,,0 0, 0, 0,0 N M M M N N a a a a a a a a a A ), (,) (,0) ( ) (, (,) (,0) ) (0, (0,) (0,0) ), ( N M F M F M F N F F F N F F F k j F ), ( k j F ), ( y x f ), ( k j F 6
11 Downsampling 024x024 32x32 Downsampled by a factor of 2 8
12 Re-Sampling Zero-Order-Hold Method (ZOH) Row and column duplication 9
13 L=256,28,64,32,6,8,4,
14 Digital Image Representation 8-bit image is commonly used Storage Human perception 32 steps (5 bits) in gray level 64 steps (6 bits) in gray level 2
15 Image Enhancement
16 Image Enhancement Goal of Image Enhancement make images more appealing no theory, ad-hoc rules, derived with insights Two Approaches Contrast Manipulation Histogram Modification 23
17 Contrast Manipulation Transfer Function Linear Nonlinear Piecewise Continuous Image Quantized Image 24
18 Contrast Manipulation Linear scaling and clipping G( j, T F( j, 0 F( j, 25
19 Contrast Manipulation Power-Law G ( j, F( j, p 0 F( j, 26
20 Contrast Manipulation Power-Law p G ( j, F( j, 0 F( j, 27
21 Contrast Manipulation Rubber Band Transfer Function Piecewise linear transformation Inflection point (control point) Can choose the area where we want to stretch or reduce the contrast 28
22 Contrast Manipulation Logarithmic Point Transformation G( j, log e af( j, log 2.0 e 0 F( j, Fourier Spectrum 0 ~ ~6.2 Useful for scaling image arrays with a very wide dynamic range 29
23 Contrast Manipulation Reverse Function G( j, F( j, 0 F( j, Able to see more details in dark areas of an image 30
24 Contrast Manipulation Inverse Function G( j, 0. F( j, 0 F( j, F( j, 3
25 Contrast Manipulation Amplitude-Level Slicing (Gray-Level Slicing) L- L- 32
26 Histogram Modification Goal Rescale the original image so that the histogram of the enhanced image follows some desired form 33
27 Histogram Modification Histogram Equalization make the output histogram to be uniformly distributed Transfer function Bucket filling 34
28 Histogram Equalization Transfer Function Input F( j, Desired (uniform) G( j, Histogram Probability Mass Function CDF Look-up Table Fi Gi F G? 35
29 Histogram Equalization Transfer Function Output histogram not really uniformly distributed Still keep the shape More flat than the original histogram 36
30 Histogram Equalization Bucket Filling F(j, # of pixels arbitrary G(j, # of pixels 0 N/256 N/256 2 N/256 uniform 255 N/256 Not - mapping N: # of total pixels Accumulated probability may not end exactly at the boundary of a bin split it out 37
31 Noise Cleaning
32 Noise Cleaning Noise electrical sensor noise photographic grain noise channel error etc. Characteristics of the noise discrete not spatially correlated higher spatial frequency 39
33 Noise Cleaning Two types of noise Uniform Noise Additive uniform noise, Gaussian noise Impulse Noise Salt and pepper noise Solutions Uniform Noise low-pass filtering Impulse Noise non-linear filtering 40
34 Basics of Spatial Filtering Mask filter, kernel, template m x n m=2a+, n=2b+, where a and b are nonnegative integers e.g. 3x3 mask W(0,0) Spatial Filtering/Convolution G( j, w(, ) F( j, k ) w(,0) F( j, w(0,0) F( j, w(,0) F( j, w(,) F( j, k ) 4
35 Basics of Spatial Filtering Q: Boundary pixels? ), ( (,) ), ( (,0) ), ( (0,0) ), (,0) ( ), ( ), ( ), ( k j F w k j F w k j F w k j F w k j F w k j G 42
36 Basics of Spatial Filtering Boundary Extension (3x3 mas copy e.g. 3x3 mask, w odd copy even Q: 5x5 mask? 43
37 Noise Cleaning Uniform noise Perform low-pass filtering General form H b b b b b b H 2 0 H 9 H e.g F 44
38 High Frequency Noise Removal Low-pass filtering Normalized to unit weighting Averaging Smaller/Larger filter size? 3x3 7x7 45
39 Noise Cleaning Impulse noise black: pixel value =0 dead sensor white: pixel value=255 saturated sensor Solutions Outlier detection Median filtering Pseudo-median filtering (PMED) 46
40 Impulse Noise Removal Outlier detection if x 8 8 O then i i 8 x 8 i O i How to choose? Larger window? 47
41 Impulse Noise Removal Median filtering a,, a N sort those values in order pick the middle one in the sorted list e.g. 3x3 mask: where N is odd 2 3 I Median is 3 48
42 Impulse Noise Removal Median filtering Preserve sharp edges Effective in removing impulse noise D/2D (directional) e.g. 2D square cross 49
43 Impulse Noise Removal e.g. D (window size = 5) Step Ramp Single Pulse Double Pulse Triple Pulse Triangle 50
44 Impulse Noise Removal Median filtering Fast computation Approximation of median e.g. 5-element filter a, b, c, d, e MED(a, b, c, d, e) =max( min(a,b,c), min(a,b,d), ) =min( max(a,b,c), max(a,b,d), ) there are 0 possible choices could be narrowed down 5
45 Impulse Noise Removal Pseudomedian filtering (PMED) e.g. 5-element filter a, b, c, d, e spatially ordered MAXMIN = A (under estimated) = max( min(a,b,c), min(b,c,d), min(c,d,e) ) MINMAX = B (over estimated) = min( max(a,b,c), max(b,c,d), max(c,d,e) ) PMED( a, b, c, d, e ) = 0.5 * ( A + B ) = 0.5 * ( MAXMIN + MINMAX ) ~ MED( a, b, c, d, e ) 52
46 Impulse Noise Removal Pseudomedian filtering (PMED) 2D case PMED 2 PMED x PMED y PMED x PMED y PMED max 2 min 2 MAXMIN ( x MINMAX ( x c c ), MAXMIN ( y ), MINMAX ( y R R ) ) 53
47 Impulse Noise Removal Pseudomedian filtering (PMED) MAXMIN Remove salt noise MINMAX Remove pepper noise May cascade two operations Remove salt and pepper noise 54
48 Impulse Noise Removal Original noisy image MAXMIN MINMAX of MAXMIN Q: same results? MINMAX MAXMIN of MINMAX 55
49 Quality Measurement Peak signal-to-noise ratio (PSNR) Mean squared error (MSE) MSE w* h j, k F' j, k The PSNR is defined as j k F 2 PSNR 0log MSE 56
50 Example Original image Gaussian noise (σ=0) PSNR : 28.8dB Gaussian noise (σ=30) PSNR : 8.8dB Q: Represent perceived visual quality? 57
Chapter 3: Intensity Transformations and Spatial Filtering
Chapter 3: Intensity Transformations and Spatial Filtering 3.1 Background 3.2 Some basic intensity transformation functions 3.3 Histogram processing 3.4 Fundamentals of spatial filtering 3.5 Smoothing
More informationVivekananda. Collegee of Engineering & Technology. Question and Answers on 10CS762 /10IS762 UNIT- 5 : IMAGE ENHANCEMENT.
Vivekananda Collegee of Engineering & Technology Question and Answers on 10CS762 /10IS762 UNIT- 5 : IMAGE ENHANCEMENT Dept. Prepared by Harivinod N Assistant Professor, of Computer Science and Engineering,
More informationLecture 4 Image Enhancement in Spatial Domain
Digital Image Processing Lecture 4 Image Enhancement in Spatial Domain Fall 2010 2 domains Spatial Domain : (image plane) Techniques are based on direct manipulation of pixels in an image Frequency Domain
More informationEECS 556 Image Processing W 09. Image enhancement. Smoothing and noise removal Sharpening filters
EECS 556 Image Processing W 09 Image enhancement Smoothing and noise removal Sharpening filters What is image processing? Image processing is the application of 2D signal processing methods to images Image
More informationIMAGING. Images are stored by capturing the binary data using some electronic devices (SENSORS)
IMAGING Film photography Digital photography Images are stored by capturing the binary data using some electronic devices (SENSORS) Sensors: Charge Coupled Device (CCD) Photo multiplier tube (PMT) The
More informationComputer Vision 2. SS 18 Dr. Benjamin Guthier Professur für Bildverarbeitung. Computer Vision 2 Dr. Benjamin Guthier
Computer Vision 2 SS 18 Dr. Benjamin Guthier Professur für Bildverarbeitung Computer Vision 2 Dr. Benjamin Guthier 1. IMAGE PROCESSING Computer Vision 2 Dr. Benjamin Guthier Content of this Chapter Non-linear
More informationImage Enhancement in Spatial Domain. By Dr. Rajeev Srivastava
Image Enhancement in Spatial Domain By Dr. Rajeev Srivastava CONTENTS Image Enhancement in Spatial Domain Spatial Domain Methods 1. Point Processing Functions A. Gray Level Transformation functions for
More informationImage Processing Lecture 10
Image Restoration Image restoration attempts to reconstruct or recover an image that has been degraded by a degradation phenomenon. Thus, restoration techniques are oriented toward modeling the degradation
More informationCHAPTER 3 IMAGE ENHANCEMENT IN THE SPATIAL DOMAIN
CHAPTER 3 IMAGE ENHANCEMENT IN THE SPATIAL DOMAIN CHAPTER 3: IMAGE ENHANCEMENT IN THE SPATIAL DOMAIN Principal objective: to process an image so that the result is more suitable than the original image
More informationDigital Image Processing. Lecture # 3 Image Enhancement
Digital Image Processing Lecture # 3 Image Enhancement 1 Image Enhancement Image Enhancement 3 Image Enhancement 4 Image Enhancement Process an image so that the result is more suitable than the original
More informationEEM 463 Introduction to Image Processing. Week 3: Intensity Transformations
EEM 463 Introduction to Image Processing Week 3: Intensity Transformations Fall 2013 Instructor: Hatice Çınar Akakın, Ph.D. haticecinarakakin@anadolu.edu.tr Anadolu University Enhancement Domains Spatial
More informationImage restoration. Restoration: Enhancement:
Image restoration Most images obtained by optical, electronic, or electro-optic means is likely to be degraded. The degradation can be due to camera misfocus, relative motion between camera and object,
More informationEE795: Computer Vision and Intelligent Systems
EE795: Computer Vision and Intelligent Systems Spring 2012 TTh 17:30-18:45 WRI C225 Lecture 04 130131 http://www.ee.unlv.edu/~b1morris/ecg795/ 2 Outline Review Histogram Equalization Image Filtering Linear
More informationAn Intuitive Explanation of Fourier Theory
An Intuitive Explanation of Fourier Theory Steven Lehar slehar@cns.bu.edu Fourier theory is pretty complicated mathematically. But there are some beautifully simple holistic concepts behind Fourier theory
More informationIMAGE ENHANCEMENT in SPATIAL DOMAIN by Intensity Transformations
It makes all the difference whether one sees darkness through the light or brightness through the shadows David Lindsay IMAGE ENHANCEMENT in SPATIAL DOMAIN by Intensity Transformations Kalyan Kumar Barik
More informationHom o om o or o phi p c Processing
Homomorphic o o Processing Motivation: Image with a large dynamic range, e.g. natural scene on a bright sunny day, recorded on a medium with a small dynamic range, e.g. a film image contrast significantly
More informationIntensity Transformations and Spatial Filtering
77 Chapter 3 Intensity Transformations and Spatial Filtering Spatial domain refers to the image plane itself, and image processing methods in this category are based on direct manipulation of pixels in
More informationDigital Image Processing, 2nd ed. Digital Image Processing, 2nd ed. The principal objective of enhancement
Chapter 3 Image Enhancement in the Spatial Domain The principal objective of enhancement to process an image so that the result is more suitable than the original image for a specific application. Enhancement
More informationImage Enhancement: To improve the quality of images
Image Enhancement: To improve the quality of images Examples: Noise reduction (to improve SNR or subjective quality) Change contrast, brightness, color etc. Image smoothing Image sharpening Modify image
More informationLinear Operations Using Masks
Linear Operations Using Masks Masks are patterns used to define the weights used in averaging the neighbors of a pixel to compute some result at that pixel Expressing linear operations on neighborhoods
More informationImage Restoration and Reconstruction
Image Restoration and Reconstruction Image restoration Objective process to improve an image, as opposed to the subjective process of image enhancement Enhancement uses heuristics to improve the image
More informationIntroduction to Digital Image Processing
Introduction to Digital Image Processing Ranga Rodrigo June 9, 29 Outline Contents Introduction 2 Point Operations 2 Histogram Processing 5 Introduction We can process images either in spatial domain or
More informationLecture 4: Spatial Domain Transformations
# Lecture 4: Spatial Domain Transformations Saad J Bedros sbedros@umn.edu Reminder 2 nd Quiz on the manipulator Part is this Fri, April 7 205, :5 AM to :0 PM Open Book, Open Notes, Focus on the material
More informationIntroduction to Digital Image Processing
Fall 2005 Image Enhancement in the Spatial Domain: Histograms, Arithmetic/Logic Operators, Basics of Spatial Filtering, Smoothing Spatial Filters Tuesday, February 7 2006, Overview (1): Before We Begin
More informationImage Restoration and Reconstruction
Image Restoration and Reconstruction Image restoration Objective process to improve an image Recover an image by using a priori knowledge of degradation phenomenon Exemplified by removal of blur by deblurring
More informationSampling and Reconstruction
Sampling and Reconstruction Sampling and Reconstruction Sampling and Spatial Resolution Spatial Aliasing Problem: Spatial aliasing is insufficient sampling of data along the space axis, which occurs because
More informationUnit - I Computer vision Fundamentals
Unit - I Computer vision Fundamentals It is an area which concentrates on mimicking human vision systems. As a scientific discipline, computer vision is concerned with the theory behind artificial systems
More informationLecture 4. Digital Image Enhancement. 1. Principle of image enhancement 2. Spatial domain transformation. Histogram processing
Lecture 4 Digital Image Enhancement 1. Principle of image enhancement 2. Spatial domain transformation Basic intensity it tranfomation ti Histogram processing Principle Objective of Enhancement Image enhancement
More informationImage Enhancement in Spatial Domain (Chapter 3)
Image Enhancement in Spatial Domain (Chapter 3) Yun Q. Shi shi@njit.edu Fall 11 Mask/Neighborhood Processing ECE643 2 1 Point Processing ECE643 3 Image Negatives S = (L 1) - r (3.2-1) Point processing
More informationBasic Algorithms for Digital Image Analysis: a course
Institute of Informatics Eötvös Loránd University Budapest, Hungary Basic Algorithms for Digital Image Analysis: a course Dmitrij Csetverikov with help of Attila Lerch, Judit Verestóy, Zoltán Megyesi,
More informationBabu Madhav Institute of Information Technology Years Integrated M.Sc.(IT)(Semester - 7)
5 Years Integrated M.Sc.(IT)(Semester - 7) 060010707 Digital Image Processing UNIT 1 Introduction to Image Processing Q: 1 Answer in short. 1. What is digital image? 1. Define pixel or picture element?
More informationFiltering Images. Contents
Image Processing and Data Visualization with MATLAB Filtering Images Hansrudi Noser June 8-9, 010 UZH, Multimedia and Robotics Summer School Noise Smoothing Filters Sigmoid Filters Gradient Filters Contents
More informationBasic relations between pixels (Chapter 2)
Basic relations between pixels (Chapter 2) Lecture 3 Basic Relationships Between Pixels Definitions: f(x,y): digital image Pixels: q, p (p,q f) A subset of pixels of f(x,y): S A typology of relations:
More informationCS4442/9542b Artificial Intelligence II prof. Olga Veksler
CS4442/9542b Artificial Intelligence II prof. Olga Veksler Lecture 2 Computer Vision Introduction, Filtering Some slides from: D. Jacobs, D. Lowe, S. Seitz, A.Efros, X. Li, R. Fergus, J. Hayes, S. Lazebnik,
More informationIT Digital Image ProcessingVII Semester - Question Bank
UNIT I DIGITAL IMAGE FUNDAMENTALS PART A Elements of Digital Image processing (DIP) systems 1. What is a pixel? 2. Define Digital Image 3. What are the steps involved in DIP? 4. List the categories of
More information1.Some Basic Gray Level Transformations
1.Some Basic Gray Level Transformations We begin the study of image enhancement techniques by discussing gray-level transformation functions.these are among the simplest of all image enhancement techniques.the
More informationIntensity Transformation and Spatial Filtering
Intensity Transformation and Spatial Filtering Outline of the Lecture Introduction. Intensity Transformation Functions. Piecewise-Linear Transformation Functions. Introduction Definition: Image enhancement
More informationImage Enhancement. Digital Image Processing, Pratt Chapter 10 (pages ) Part 1: pixel-based operations
Image Enhancement Digital Image Processing, Pratt Chapter 10 (pages 243-261) Part 1: pixel-based operations Image Processing Algorithms Spatial domain Operations are performed in the image domain Image
More informationLast update: May 4, Vision. CMSC 421: Chapter 24. CMSC 421: Chapter 24 1
Last update: May 4, 200 Vision CMSC 42: Chapter 24 CMSC 42: Chapter 24 Outline Perception generally Image formation Early vision 2D D Object recognition CMSC 42: Chapter 24 2 Perception generally Stimulus
More informationDigital Image Processing
Digital Image Processing Third Edition Rafael C. Gonzalez University of Tennessee Richard E. Woods MedData Interactive PEARSON Prentice Hall Pearson Education International Contents Preface xv Acknowledgments
More informationIslamic University of Gaza Faculty of Engineering Computer Engineering Department
Islamic University of Gaza Faculty of Engineering Computer Engineering Department EELE 5310: Digital Image Processing Spring 2011 Date: May 29, 2011 Time : 120 minutes Final Exam Student Name: Student
More informationDigital Image Processing
Digital Image Processing Part 2: Image Enhancement in the Spatial Domain AASS Learning Systems Lab, Dep. Teknik Room T1209 (Fr, 11-12 o'clock) achim.lilienthal@oru.se Course Book Chapter 3 2011-04-06 Contents
More informationCS4442/9542b Artificial Intelligence II prof. Olga Veksler
CS4442/9542b Artificial Intelligence II prof. Olga Veksler Lecture 8 Computer Vision Introduction, Filtering Some slides from: D. Jacobs, D. Lowe, S. Seitz, A.Efros, X. Li, R. Fergus, J. Hayes, S. Lazebnik,
More informationChapter - 2 : IMAGE ENHANCEMENT
Chapter - : IMAGE ENHANCEMENT The principal objective of enhancement technique is to process a given image so that the result is more suitable than the original image for a specific application Image Enhancement
More information2D Image Processing INFORMATIK. Kaiserlautern University. DFKI Deutsches Forschungszentrum für Künstliche Intelligenz
2D Image Processing - Filtering Prof. Didier Stricker Kaiserlautern University http://ags.cs.uni-kl.de/ DFKI Deutsches Forschungszentrum für Künstliche Intelligenz http://av.dfki.de 1 What is image filtering?
More informationWhat is an Image? Image Acquisition. Image Processing - Lesson 2. An image is a projection of a 3D scene into a 2D projection plane.
mage Processing - Lesson 2 mage Acquisition mage Characteristics mage Acquisition mage Digitization Sampling Quantization mage Histogram What is an mage? An image is a projection of a 3D scene into a 2D
More informationFiltering and Enhancing Images
KECE471 Computer Vision Filtering and Enhancing Images Chang-Su Kim Chapter 5, Computer Vision by Shapiro and Stockman Note: Some figures and contents in the lecture notes of Dr. Stockman are used partly.
More informationImage Processing. Bilkent University. CS554 Computer Vision Pinar Duygulu
Image Processing CS 554 Computer Vision Pinar Duygulu Bilkent University Today Image Formation Point and Blob Processing Binary Image Processing Readings: Gonzalez & Woods, Ch. 3 Slides are adapted from
More informationLecture 2 Image Processing and Filtering
Lecture 2 Image Processing and Filtering UW CSE vision faculty What s on our plate today? Image formation Image sampling and quantization Image interpolation Domain transformations Affine image transformations
More informationImage Enhancement 3-1
Image Enhancement The goal of image enhancement is to improve the usefulness of an image for a given task, such as providing a more subjectively pleasing image for human viewing. In image enhancement,
More informationDigital Image Processing. Prof. P. K. Biswas. Department of Electronic & Electrical Communication Engineering
Digital Image Processing Prof. P. K. Biswas Department of Electronic & Electrical Communication Engineering Indian Institute of Technology, Kharagpur Lecture - 21 Image Enhancement Frequency Domain Processing
More informationDigital Image Processing
Digital Image Processing Intensity Transformations (Point Processing) Christophoros Nikou cnikou@cs.uoi.gr University of Ioannina - Department of Computer Science and Engineering 2 Intensity Transformations
More informationComputer Vision I - Basics of Image Processing Part 1
Computer Vision I - Basics of Image Processing Part 1 Carsten Rother 28/10/2014 Computer Vision I: Basics of Image Processing Link to lectures Computer Vision I: Basics of Image Processing 28/10/2014 2
More informationDigital Image Processing
Digital Image Processing Jen-Hui Chuang Department of Computer Science National Chiao Tung University 2 3 Image Enhancement in the Spatial Domain 3.1 Background 3.4 Enhancement Using Arithmetic/Logic Operations
More informationMotivation. Intensity Levels
Motivation Image Intensity and Point Operations Dr. Edmund Lam Department of Electrical and Electronic Engineering The University of Hong ong A digital image is a matrix of numbers, each corresponding
More informationFundamentals of Digital Image Processing
\L\.6 Gw.i Fundamentals of Digital Image Processing A Practical Approach with Examples in Matlab Chris Solomon School of Physical Sciences, University of Kent, Canterbury, UK Toby Breckon School of Engineering,
More informationDigital Image Processing. Introduction
Digital Image Processing Introduction Digital Image Definition An image can be defined as a twodimensional function f(x,y) x,y: Spatial coordinate F: the amplitude of any pair of coordinate x,y, which
More informationIntensity Transformations. Digital Image Processing. What Is Image Enhancement? Contents. Image Enhancement Examples. Intensity Transformations
Digital Image Processing 2 Intensity Transformations Intensity Transformations (Point Processing) Christophoros Nikou cnikou@cs.uoi.gr It makes all the difference whether one sees darkness through the
More informationMotivation. Gray Levels
Motivation Image Intensity and Point Operations Dr. Edmund Lam Department of Electrical and Electronic Engineering The University of Hong ong A digital image is a matrix of numbers, each corresponding
More informationAn Introduc+on to Mathema+cal Image Processing IAS, Park City Mathema2cs Ins2tute, Utah Undergraduate Summer School 2010
An Introduc+on to Mathema+cal Image Processing IAS, Park City Mathema2cs Ins2tute, Utah Undergraduate Summer School 2010 Luminita Vese Todd WiCman Department of Mathema2cs, UCLA lvese@math.ucla.edu wicman@math.ucla.edu
More informationPoint and Spatial Processing
Filtering 1 Point and Spatial Processing Spatial Domain g(x,y) = T[ f(x,y) ] f(x,y) input image g(x,y) output image T is an operator on f Defined over some neighborhood of (x,y) can operate on a set of
More informationDigital Image Processing. Image Enhancement (Point Processing)
Digital Image Processing Image Enhancement (Point Processing) 2 Contents In this lecture we will look at image enhancement point processing techniques: What is point processing? Negative images Thresholding
More informationDigital Image Processing
Digital Image Processing Part 9: Representation and Description AASS Learning Systems Lab, Dep. Teknik Room T1209 (Fr, 11-12 o'clock) achim.lilienthal@oru.se Course Book Chapter 11 2011-05-17 Contents
More informationIn this lecture. Background. Background. Background. PAM3012 Digital Image Processing for Radiographers
PAM3012 Digital Image Processing for Radiographers Image Enhancement in the Spatial Domain (Part I) In this lecture Image Enhancement Introduction to spatial domain Information Greyscale transformations
More informationPerception. Autonomous Mobile Robots. Sensors Vision Uncertainties, Line extraction from laser scans. Autonomous Systems Lab. Zürich.
Autonomous Mobile Robots Localization "Position" Global Map Cognition Environment Model Local Map Path Perception Real World Environment Motion Control Perception Sensors Vision Uncertainties, Line extraction
More informationImplementation of efficient Image Enhancement Factor using Modified Decision Based Unsymmetric Trimmed Median Filter
Implementation of efficient Image Enhancement Factor using Modified Decision Based Unsymmetric Trimmed Median Filter R.Himabindu Abstract: A.SUJATHA, ASSISTANT PROFESSOR IN G.PULLAIAH COLLEGE OF ENGINEERING
More informationNoise Model. Important Noise Probability Density Functions (Cont.) Important Noise Probability Density Functions
Others -- Noise Removal Techniques -- Edge Detection Techniques -- Geometric Operations -- Color Image Processing -- Color Spaces Xiaojun Qi Noise Model The principal sources of noise in digital images
More informationImage Processing. Traitement d images. Yuliya Tarabalka Tel.
Traitement d images Yuliya Tarabalka yuliya.tarabalka@hyperinet.eu yuliya.tarabalka@gipsa-lab.grenoble-inp.fr Tel. 04 76 82 62 68 Noise reduction Image restoration Restoration attempts to reconstruct an
More informationA NEW ROBUST IMAGE WATERMARKING SCHEME BASED ON DWT WITH SVD
A NEW ROBUST IMAGE WATERMARKING SCHEME BASED ON WITH S.Shanmugaprabha PG Scholar, Dept of Computer Science & Engineering VMKV Engineering College, Salem India N.Malmurugan Director Sri Ranganathar Institute
More informationINTENSITY TRANSFORMATION AND SPATIAL FILTERING
1 INTENSITY TRANSFORMATION AND SPATIAL FILTERING Lecture 3 Image Domains 2 Spatial domain Refers to the image plane itself Image processing methods are based and directly applied to image pixels Transform
More informationSURVEY ON IMAGE PROCESSING IN THE FIELD OF DE-NOISING TECHNIQUES AND EDGE DETECTION TECHNIQUES ON RADIOGRAPHIC IMAGES
SURVEY ON IMAGE PROCESSING IN THE FIELD OF DE-NOISING TECHNIQUES AND EDGE DETECTION TECHNIQUES ON RADIOGRAPHIC IMAGES 1 B.THAMOTHARAN, 2 M.MENAKA, 3 SANDHYA VAIDYANATHAN, 3 SOWMYA RAVIKUMAR 1 Asst. Prof.,
More informationECG782: Multidimensional Digital Signal Processing
Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu ECG782: Multidimensional Digital Signal Processing Spatial Domain Filtering http://www.ee.unlv.edu/~b1morris/ecg782/ 2 Outline Background Intensity
More informationECG782: Multidimensional Digital Signal Processing
Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu ECG782: Multidimensional Digital Signal Processing Spring 2014 TTh 14:30-15:45 CBC C313 Lecture 03 Image Processing Basics 13/01/28 http://www.ee.unlv.edu/~b1morris/ecg782/
More informationDigital Image Processing
Digital Image Processing Image Restoration and Reconstruction (Noise Removal) Christophoros Nikou cnikou@cs.uoi.gr University of Ioannina - Department of Computer Science and Engineering 2 Image Restoration
More information(0, 1, 1) (0, 1, 1) (0, 1, 0) What is light? What is color? Terminology
lecture 23 (0, 1, 1) (0, 0, 0) (0, 0, 1) (0, 1, 1) (1, 1, 1) (1, 1, 0) (0, 1, 0) hue - which ''? saturation - how pure? luminance (value) - intensity What is light? What is? Light consists of electromagnetic
More informationIntroduction to Computer Vision. Human Eye Sampling
Human Eye Sampling Sampling Rough Idea: Ideal Case 23 "Digitized Image" "Continuous Image" Dirac Delta Function 2D "Comb" δ(x,y) = 0 for x = 0, y= 0 s δ(x,y) dx dy = 1 f(x,y)δ(x-a,y-b) dx dy = f(a,b) δ(x-ns,y-ns)
More informationImage Acquisition + Histograms
Image Processing - Lesson 1 Image Acquisition + Histograms Image Characteristics Image Acquisition Image Digitization Sampling Quantization Histograms Histogram Equalization What is an Image? An image
More informationLecture Image Enhancement and Spatial Filtering
Lecture Image Enhancement and Spatial Filtering Harvey Rhody Chester F. Carlson Center for Imaging Science Rochester Institute of Technology rhody@cis.rit.edu September 29, 2005 Abstract Applications of
More informationPSD2B Digital Image Processing. Unit I -V
PSD2B Digital Image Processing Unit I -V Syllabus- Unit 1 Introduction Steps in Image Processing Image Acquisition Representation Sampling & Quantization Relationship between pixels Color Models Basics
More informationEELE 5310: Digital Image Processing. Lecture 2 Ch. 3. Eng. Ruba A. Salamah. iugaza.edu
EELE 5310: Digital Image Processing Lecture 2 Ch. 3 Eng. Ruba A. Salamah Rsalamah @ iugaza.edu 1 Image Enhancement in the Spatial Domain 2 Lecture Reading 3.1 Background 3.2 Some Basic Gray Level Transformations
More informationIMAGE DE-NOISING IN WAVELET DOMAIN
IMAGE DE-NOISING IN WAVELET DOMAIN Aaditya Verma a, Shrey Agarwal a a Department of Civil Engineering, Indian Institute of Technology, Kanpur, India - (aaditya, ashrey)@iitk.ac.in KEY WORDS: Wavelets,
More informationCS4733 Class Notes, Computer Vision
CS4733 Class Notes, Computer Vision Sources for online computer vision tutorials and demos - http://www.dai.ed.ac.uk/hipr and Computer Vision resources online - http://www.dai.ed.ac.uk/cvonline Vision
More informationBiometrics Technology: Image Processing & Pattern Recognition (by Dr. Dickson Tong)
Biometrics Technology: Image Processing & Pattern Recognition (by Dr. Dickson Tong) References: [1] http://homepages.inf.ed.ac.uk/rbf/hipr2/index.htm [2] http://www.cs.wisc.edu/~dyer/cs540/notes/vision.html
More informationComputer Vision. The image formation process
Computer Vision The image formation process Filippo Bergamasco (filippo.bergamasco@unive.it) http://www.dais.unive.it/~bergamasco DAIS, Ca Foscari University of Venice Academic year 2016/2017 The image
More informationIMAGE ENHANCEMENT IN THE SPATIAL DOMAIN
1 Image Enhancement in the Spatial Domain 3 IMAGE ENHANCEMENT IN THE SPATIAL DOMAIN Unit structure : 3.0 Objectives 3.1 Introduction 3.2 Basic Grey Level Transform 3.3 Identity Transform Function 3.4 Image
More informationImage Processing. Filtering. Slide 1
Image Processing Filtering Slide 1 Preliminary Image generation Original Noise Image restoration Result Slide 2 Preliminary Classic application: denoising However: Denoising is much more than a simple
More informationINTRODUCTION TO IMAGE PROCESSING (COMPUTER VISION)
INTRODUCTION TO IMAGE PROCESSING (COMPUTER VISION) Revision: 1.4, dated: November 10, 2005 Tomáš Svoboda Czech Technical University, Faculty of Electrical Engineering Center for Machine Perception, Prague,
More informationReversible Wavelets for Embedded Image Compression. Sri Rama Prasanna Pavani Electrical and Computer Engineering, CU Boulder
Reversible Wavelets for Embedded Image Compression Sri Rama Prasanna Pavani Electrical and Computer Engineering, CU Boulder pavani@colorado.edu APPM 7400 - Wavelets and Imaging Prof. Gregory Beylkin -
More informationImage Processing. Daniel Danilov July 13, 2015
Image Processing Daniel Danilov July 13, 2015 Overview 1. Principle of digital images and filters 2. Basic examples of filters 3. Edge detection and segmentation 1 / 25 Motivation For what image processing
More informationEELE 5310: Digital Image Processing. Ch. 3. Eng. Ruba A. Salamah. iugaza.edu
EELE 531: Digital Image Processing Ch. 3 Eng. Ruba A. Salamah Rsalamah @ iugaza.edu 1 Image Enhancement in the Spatial Domain 2 Lecture Reading 3.1 Background 3.2 Some Basic Gray Level Transformations
More informationJNTUWORLD. 4. Prove that the average value of laplacian of the equation 2 h = ((r2 σ 2 )/σ 4 ))exp( r 2 /2σ 2 ) is zero. [16]
Code No: 07A70401 R07 Set No. 2 1. (a) What are the basic properties of frequency domain with respect to the image processing. (b) Define the terms: i. Impulse function of strength a ii. Impulse function
More informationPoint Operations. Prof. George Wolberg Dept. of Computer Science City College of New York
Point Operations Prof. George Wolberg Dept. of Computer Science City College of New York Objectives In this lecture we describe point operations commonly used in image processing: - Thresholding - Quantization
More informationDigital Image Analysis and Processing
Digital Image Analysis and Processing CPE 0907544 Image Enhancement Part I Intensity Transformation Chapter 3 Sections: 3.1 3.3 Dr. Iyad Jafar Outline What is Image Enhancement? Background Intensity Transformation
More informationDigital Image Processing
Digital Image Processing 7. Color Transforms 15110191 Keuyhong Cho Non-linear Color Space Reflect human eye s characters 1) Use uniform color space 2) Set distance of color space has same ratio difference
More informationImage Acquisition Image Digitization Spatial domain Intensity domain Image Characteristics
Image Acquisition Image Digitization Spatial domain Intensity domain Image Characteristics 1 What is an Image? An image is a projection of a 3D scene into a 2D projection plane. An image can be defined
More informationPoint operation Spatial operation Transform operation Pseudocoloring
Image Enhancement Introduction Enhancement by point processing Simple intensity transformation Histogram processing Spatial filtering Smoothing filters Sharpening filters Enhancement in the frequency domain
More informationAn educational software package for digital image processing
An educational software package for digital image processing Juan Manuel ~amirez', Pilar Gomez-~il~, Luis G. Guerrero', David ~aez' & Ernesto ~orantes' '~e~artamento de Ingenieria Electrdnica, Universidad
More informationImage Processing Fundamentals. Nicolas Vazquez Principal Software Engineer National Instruments
Image Processing Fundamentals Nicolas Vazquez Principal Software Engineer National Instruments Agenda Objectives and Motivations Enhancing Images Checking for Presence Locating Parts Measuring Features
More informationELEC Dr Reji Mathew Electrical Engineering UNSW
ELEC 4622 Dr Reji Mathew Electrical Engineering UNSW Dynamic Range and Weber s Law HVS is capable of operating over an enormous dynamic range, However, sensitivity is far from uniform over this range Example:
More information