Interpolation of RGB components in Bayer CFA images
|
|
|
- Silas Cooper
- 9 years ago
- Views:
Transcription
1 Interpolation of RGB components in Bayer CFA images Demosaicing of Bayer-sampled color images Problem: Most digital color cameras, capture only one color component at each spatial location. The remaining components must be reconstructed by interpolation from the captured samples. Objective: Develop algorithms to interpolate each color plane (called demosaicing) with best quality reconstruction, and with minimal computational complexity. 1
2 Construction of color image from color planes + original 2
3 red original green original 3
4 blue original Formation of Color planes 4
5 red subsampled green subsampled 5
6 blue subsampled Bayer CFA image 6
7 Color plane interpolation Green channel: bilinear interpolation G A G L G R G I G B 1 G I = ( GL + GR + GB + G 4 A ) Color plane interpolation Red channel: bilinear interpolation R NW R NE R C R SW R SE R S 1 R C = ( RNW + RNE + RSW + R 4 SE ) S ( R R ) 1 R = + 2 SW SE 7
8 red interpolated green interpolated 8
9 blue interpolated Interpolated color image 9
10 original Can we do better? Color planes have severe aliasing. Better interpolation of the individual planes has little effect. 10
11 red interpolated with bilinear interpolator red interpolated with bicubic interpolator 11
12 Can we do better? Color planes have severe aliasing. Better interpolation of the individual planes has little effect. We could optically prefilter the image (blur it) so that aliasing is less severe. red interpolated with bilinear interpolator 12
13 prefiltered red interpolated with bilinear interpolator Interpolated color image 13
14 Prefiltered Interpolated color image original 14
15 Demosaicing Approaches Non-Adaptive Single-Channel Interpolation: Interpolate each color channel separately using a standard technique, such as nearest-neighbor interpolation, bilinear interpolation, etc. Edge-Directed Interpolation: Estimate potential edges, avoid interpolating across the edges. Edge-directed interpolation 3 1 x Calculate horizontal gradient ΔH = G1 G2 2.Calculate vertical gradient ΔV = G3 G4 3.If ΔH > ΔV, Gx = (G3 + G4)/2 Else if ΔH < ΔV, Gx = (G1 + G2)/2 Else Gx = (G1 + G2 + G3 + G4)/4 Demosaicing Approaches Edge-Directed Interpolation: Based on the assumption that color channels have similar texture, various edge detectors can be used Edge-directed interpolation 1. Calculate horizontal gradient ΔH = (R3 + R7)/2 R5 2. Calculate vertical gradient ΔV = (R1 + R9)/2 R5 3. If ΔH > ΔV, G5 = (G2 + G8)/2 Else if ΔH < ΔV, G5 = (G4 + G6)/2 Else G5 = (G2 + G8 + G4 + G6)/4 15
16 Demosaicing Approaches Constant-Hue-Based Interpolation: Hue does not change abruptly within a small neighborhood. Interpolate green channel first. Interpolate hue (defined as either color differences or color ratios). Estimate the missing (red/blue) from the interpolated hue. Red Interpolate Interpolate d Red Green Interpolate Demosaicing Approaches Edge-Directed Interpolation of Hue: It is a combination of edge-directed interpolation and constant-hue-based interpolation. Hue is interpolated as in constant-hue-based interpolation approach, but this time, hue is interpolated based on the edge directions (as in the edge-directed interpolation algorithm). 16
17 Demosaicing Approaches Using Laplacian For Enhancement: Use the second-order gradients of red/blue channels to enhance green channel Calculate horizontal gradient ΔH = G4 G6 + R5 R3 + R5 R7 2. Calculate vertical gradient ΔV = G2 G8 + R5 R1 + R5 R9 3. If ΔH > ΔV, G5 = (G2 + G8)/2 + (R5 R1 + R5 R9)/4 Else if ΔH < ΔV, G5 = (G4 + G6)/2 + (R5 R3 + R5 R7)/4 Else G5 = (G2 + G8 + G4 + G6)/4 + (R5 R1 + R5 R9 + R5 R3 + R5 R7)/8 Demosaicing CFA s bilinear interpolation Interpolation of green pixels G8 = (G3+G7+G9+G13) / 4 Interpolation of red/blue pixels : * Interpolation of a red/blue pixel at a green position: B7 = (B6+B8) / 2 R7 = (R2+R12) / 2 * Interpolation of a red/blue pixel at a blue/red position: R8 = (R2+R4+R12+R14) / 4 B12 = (B6+B8+B16+B18) / 4 17
18 Demosaicing CFA s Constant hue-based interpolation (Cok) Hue: Interpolate G first Demosaicing CFA s Median-based interpolation (Freeman) 1. Linear interpolation 2. Median filter on color differences 18
19 Demosaicing CFA s Gradient-based interpolation (LaRoche-Prescott) 1. Interpolation on G Demosaicing CFA s Gradient-based interpolation (LaRoche-Prescott) 2. Interpolation of color differences 19
20 Demosaicing CFA s bilinear Cok Freeman LaRoche Demosaicking CFA s Generally, Freeman s is the best, especially for natural images. 20
21 A Valuable Resource: A Study of Spatial Color Interpolation Algorithms for Single-Detector Digital Cameras 1/projects/99/tingchen/ URL on Resources page This week in Lab Implement Bilinear Interpolation Implement one other Non-Adaptive Algorithm Implement one Adaptive Algorithm 21
How To Demosaice With A Cfa
Demosaicing by Smoothing along 1D Features Boris Ajdin Matthias B. Hullin Christian Fuchs Hans-Peter Seidel Hendrik P. A. Lensch MPI Informatik Saarbrücken Germany {bajdin,hullin,cfuchs,hpseidel,lensch}@mpi-inf.mpg.de
Investigation of Color Aliasing of High Spatial Frequencies and Edges for Bayer-Pattern Sensors and Foveon X3 Direct Image Sensors
Investigation of Color Aliasing of High Spatial Frequencies and Edges for Bayer-Pattern Sensors and Foveon X3 Direct Image Sensors Rudolph J. Guttosch Foveon, Inc. Santa Clara, CA Abstract The reproduction
Lecture 12: Cameras and Geometry. CAP 5415 Fall 2010
Lecture 12: Cameras and Geometry CAP 5415 Fall 2010 The midterm What does the response of a derivative filter tell me about whether there is an edge or not? Things aren't working Did you look at the filters?
jorge s. marques image processing
image processing images images: what are they? what is shown in this image? What is this? what is an image images describe the evolution of physical variables (intensity, color, reflectance, condutivity)
CMOS Image Sensor Noise Reduction Method for Image Signal Processor in Digital Cameras and Camera Phones
CMOS Image Sensor Noise Reduction Method for Image Signal Processor in Digital Cameras and Camera Phones Youngjin Yoo, SeongDeok Lee, Wonhee Choe and Chang-Yong Kim Display and Image Processing Laboratory,
RESOLUTION IMPROVEMENT OF DIGITIZED IMAGES
Proceedings of ALGORITMY 2005 pp. 270 279 RESOLUTION IMPROVEMENT OF DIGITIZED IMAGES LIBOR VÁŠA AND VÁCLAV SKALA Abstract. A quick overview of preprocessing performed by digital still cameras is given
High Quality Image Deblurring Panchromatic Pixels
High Quality Image Deblurring Panchromatic Pixels ACM Transaction on Graphics vol. 31, No. 5, 2012 Sen Wang, Tingbo Hou, John Border, Hong Qin, and Rodney Miller Presented by Bong-Seok Choi School of Electrical
ROBUST COLOR JOINT MULTI-FRAME DEMOSAICING AND SUPER- RESOLUTION ALGORITHM
ROBUST COLOR JOINT MULTI-FRAME DEMOSAICING AND SUPER- RESOLUTION ALGORITHM Theodor Heinze Hasso-Plattner-Institute for Software Systems Engineering Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Germany [email protected]
PERFORMANCE ANALYSIS OF HIGH RESOLUTION IMAGES USING INTERPOLATION TECHNIQUES IN MULTIMEDIA COMMUNICATION SYSTEM
PERFORMANCE ANALYSIS OF HIGH RESOLUTION IMAGES USING INTERPOLATION TECHNIQUES IN MULTIMEDIA COMMUNICATION SYSTEM Apurva Sinha 1, Mukesh kumar 2, A.K. Jaiswal 3, Rohini Saxena 4 Department of Electronics
Linear minimum mean square error demosaicking
Chapter 1 Linear minimum mean square error demosaicking DAVID ALLEYSSON Laboratoire de Psychologie et Neurocognition Université Pierre-Mendès-France - CNRS Grenoble, France Email: [email protected]
Resolution for Color photography
Resolution for Color photography Paul M. Hubel a and Markus Bautsch b a Foveon, Inc., 282 San Tomas Expressway, Santa Clara, CA, USA 955; b Stiftung Warentest, Luetzowplatz -3, D-785 Berlin-Tiergarten,
Computational Foundations of Cognitive Science
Computational Foundations of Cognitive Science Lecture 15: Convolutions and Kernels Frank Keller School of Informatics University of Edinburgh [email protected] February 23, 2010 Frank Keller Computational
Sachin Patel HOD I.T Department PCST, Indore, India. Parth Bhatt I.T Department, PCST, Indore, India. Ankit Shah CSE Department, KITE, Jaipur, India
Image Enhancement Using Various Interpolation Methods Parth Bhatt I.T Department, PCST, Indore, India Ankit Shah CSE Department, KITE, Jaipur, India Sachin Patel HOD I.T Department PCST, Indore, India
T O B C A T C A S E G E O V I S A T DETECTIE E N B L U R R I N G V A N P E R S O N E N IN P A N O R A MISCHE BEELDEN
T O B C A T C A S E G E O V I S A T DETECTIE E N B L U R R I N G V A N P E R S O N E N IN P A N O R A MISCHE BEELDEN Goal is to process 360 degree images and detect two object categories 1. Pedestrians,
Choosing a digital camera for your microscope John C. Russ, Materials Science and Engineering Dept., North Carolina State Univ.
Choosing a digital camera for your microscope John C. Russ, Materials Science and Engineering Dept., North Carolina State Univ., Raleigh, NC One vital step is to choose a transfer lens matched to your
Computer Vision: Machine Vision Filters. Computer Vision. Optical Filters. 25 August 2014
Computer Vision Optical Filters 25 August 2014 Copyright 2001 2014 by NHL Hogeschool, Van de Loosdrecht Machine Vision BV and Klaas Dijkstra All rights reserved [email protected], [email protected],
Real-time 3D Scanning System for Pavement Distortion Inspection
Real-time 3D Scanning System for Pavement Distortion Inspection Bugao Xu, Ph.D. & Professor University of Texas at Austin Center for Transportation Research Austin, Texas 78712 Pavement distress categories
Admin stuff. 4 Image Pyramids. Spatial Domain. Projects. Fourier domain 2/26/2008. Fourier as a change of basis
Admin stuff 4 Image Pyramids Change of office hours on Wed 4 th April Mon 3 st March 9.3.3pm (right after class) Change of time/date t of last class Currently Mon 5 th May What about Thursday 8 th May?
Superresolution images reconstructed from aliased images
Superresolution images reconstructed from aliased images Patrick Vandewalle, Sabine Süsstrunk and Martin Vetterli LCAV - School of Computer and Communication Sciences Ecole Polytechnique Fédérale de Lausanne
Green = 0,255,0 (Target Color for E.L. Gray Construction) CIELAB RGB Simulation Result for E.L. Gray Match (43,215,35) Equal Luminance Gray for Green
Red = 255,0,0 (Target Color for E.L. Gray Construction) CIELAB RGB Simulation Result for E.L. Gray Match (184,27,26) Equal Luminance Gray for Red = 255,0,0 (147,147,147) Mean of Observer Matches to Red=255
Canny Edge Detection
Canny Edge Detection 09gr820 March 23, 2009 1 Introduction The purpose of edge detection in general is to significantly reduce the amount of data in an image, while preserving the structural properties
Visualization and Feature Extraction, FLOW Spring School 2016 Prof. Dr. Tino Weinkauf. Flow Visualization. Image-Based Methods (integration-based)
Visualization and Feature Extraction, FLOW Spring School 2016 Prof. Dr. Tino Weinkauf Flow Visualization Image-Based Methods (integration-based) Spot Noise (Jarke van Wijk, Siggraph 1991) Flow Visualization:
Assessment. Presenter: Yupu Zhang, Guoliang Jin, Tuo Wang Computer Vision 2008 Fall
Automatic Photo Quality Assessment Presenter: Yupu Zhang, Guoliang Jin, Tuo Wang Computer Vision 2008 Fall Estimating i the photorealism of images: Distinguishing i i paintings from photographs h Florin
Using visible SNR (vsnr) to compare image quality of pixel binning and digital resizing
Using visible SNR (vsnr) to compare image quality of pixel binning and digital resizing Joyce Farrell a, Mike Okincha b, Manu Parmar ac, and Brian Wandell ac a Dept. of Electrical Engineering, Stanford
Multispectral stereo acquisition using 2 RGB cameras and color filters: color and disparity accuracy
Multispectral stereo acquisition using 2 RGB cameras and color filters: color and disparity accuracy (a) and Bernhard Hill (b) (a) Institute of Imaging and Computer Vision (b) Research Group Color and
Chapter 1 Simultaneous demosaicing and resolution enhancement from under-sampled image sequences
Chapter Simultaneous demosaicing and resolution enhancement from under-sampled image sequences SINA FARSIU Duke University Eye Center Durham, NC Email: [email protected] DIRK ROBINSON Ricoh Innovations
Armstrong Atlantic State University Engineering Studies MATLAB Marina Image Processing Primer
Armstrong Atlantic State University Engineering Studies MATLAB Marina Image Processing Primer Prerequisites The Image Processing Primer assumes nowledge of the MATLAB IDE, MATLAB help, arithmetic operations,
NEW 35MM CMOS IMAGE SENSOR FOR DIGITAL CINE MOTION IMAGING
WHITE PAPER NEW 35MM CMOS IMAGE SENSOR FOR DIGITAL CINE MOTION IMAGING Written by Larry Thorpe Professional Engineering & Solutions Division, Canon U.S.A., Inc. For more info: cinemaeos.usa.canon.com !
Lab #8: Introduction to ENVI (Environment for Visualizing Images) Image Processing
Lab #8: Introduction to ENVI (Environment for Visualizing Images) Image Processing ASSIGNMENT: Display each band of a satellite image as a monochrome image and combine three bands into a color image, and
Using Image J to Measure the Brightness of Stars (Written by Do H. Kim)
Using Image J to Measure the Brightness of Stars (Written by Do H. Kim) What is Image J? Image J is Java-based image processing program developed at the National Institutes of Health. Image J runs on everywhere,
Diagnostics for Digital Capture using MTF
Diagnostics for Digital Capture using MTF Don Williams and Peter D. Burns Eastman Kodak Company Rochester, NY USA Abstract The function (MTF) has long been used as a diagnostic tool for analog image capture,
A Comprehensive Set of Image Quality Metrics
The Gold Standard of image quality specification and verification A Comprehensive Set of Image Quality Metrics GoldenThread is the product of years of research and development conducted for the Federal
A Short Introduction to Computer Graphics
A Short Introduction to Computer Graphics Frédo Durand MIT Laboratory for Computer Science 1 Introduction Chapter I: Basics Although computer graphics is a vast field that encompasses almost any graphical
Analecta Vol. 8, No. 2 ISSN 2064-7964
EXPERIMENTAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS IN ENGINEERING PROCESSING SYSTEM S. Dadvandipour Institute of Information Engineering, University of Miskolc, Egyetemváros, 3515, Miskolc, Hungary,
Lecture 16: A Camera s Image Processing Pipeline Part 1. Kayvon Fatahalian CMU 15-869: Graphics and Imaging Architectures (Fall 2011)
Lecture 16: A Camera s Image Processing Pipeline Part 1 Kayvon Fatahalian CMU 15-869: Graphics and Imaging Architectures (Fall 2011) Today (actually all week) Operations that take photons to an image Processing
Image Interpolation by Pixel Level Data-Dependent Triangulation
Volume xx (200y), Number z, pp. 1 7 Image Interpolation by Pixel Level Data-Dependent Triangulation Dan Su, Philip Willis Department of Computer Science, University of Bath, Bath, BA2 7AY, U.K. mapds,
High Definition (HD) Image Formats for Television Production
EBU TECH 3299 High Definition (HD) Image Formats for Television Production Status: Specification Geneva January 2010 1 Page intentionally left blank. This document is paginated for two sided printing Tech
BCC Multi Stripe Wipe
BCC Multi Stripe Wipe The BCC Multi Stripe Wipe is a similar to a Horizontal or Vertical Blind wipe. It offers extensive controls to randomize the stripes parameters. The following example shows a Multi
REAL TIME TRAFFIC LIGHT CONTROL USING IMAGE PROCESSING
REAL TIME TRAFFIC LIGHT CONTROL USING IMAGE PROCESSING Ms.PALLAVI CHOUDEKAR Ajay Kumar Garg Engineering College, Department of electrical and electronics Ms.SAYANTI BANERJEE Ajay Kumar Garg Engineering
WHITE PAPER. Are More Pixels Better? www.basler-ipcam.com. Resolution Does it Really Matter?
WHITE PAPER www.basler-ipcam.com Are More Pixels Better? The most frequently asked question when buying a new digital security camera is, What resolution does the camera provide? The resolution is indeed
Robust and accurate global vision system for real time tracking of multiple mobile robots
Robust and accurate global vision system for real time tracking of multiple mobile robots Mišel Brezak Ivan Petrović Edouard Ivanjko Department of Control and Computer Engineering, Faculty of Electrical
EFX Keying/Alpha plugins for After Effects
EFX Keying/Alpha plugins for After Effects Here you'll find description of the plugins developed to help select pixels based on many criteria. Also after selection, there are often things you want to do
Implementation of Canny Edge Detector of color images on CELL/B.E. Architecture.
Implementation of Canny Edge Detector of color images on CELL/B.E. Architecture. Chirag Gupta,Sumod Mohan K [email protected], [email protected] Abstract In this project we propose a method to improve
Single Depth Image Super Resolution and Denoising Using Coupled Dictionary Learning with Local Constraints and Shock Filtering
Single Depth Image Super Resolution and Denoising Using Coupled Dictionary Learning with Local Constraints and Shock Filtering Jun Xie 1, Cheng-Chuan Chou 2, Rogerio Feris 3, Ming-Ting Sun 1 1 University
Digitization of Old Maps Using Deskan Express 5.0
Dražen Tutić *, Miljenko Lapaine ** Digitization of Old Maps Using Deskan Express 5.0 Keywords: digitization; scanner; scanning; old maps; Deskan Express 5.0. Summary The Faculty of Geodesy, University
Assessment of Camera Phone Distortion and Implications for Watermarking
Assessment of Camera Phone Distortion and Implications for Watermarking Aparna Gurijala, Alastair Reed and Eric Evans Digimarc Corporation, 9405 SW Gemini Drive, Beaverton, OR 97008, USA 1. INTRODUCTION
The Image Deblurring Problem
page 1 Chapter 1 The Image Deblurring Problem You cannot depend on your eyes when your imagination is out of focus. Mark Twain When we use a camera, we want the recorded image to be a faithful representation
Correcting the Lateral Response Artifact in Radiochromic Film Images from Flatbed Scanners
Correcting the Lateral Response Artifact in Radiochromic Film Images from Flatbed Scanners Background The lateral response artifact (LRA) in radiochromic film images from flatbed scanners was first pointed
JPEG compression of monochrome 2D-barcode images using DCT coefficient distributions
Edith Cowan University Research Online ECU Publications Pre. JPEG compression of monochrome D-barcode images using DCT coefficient distributions Keng Teong Tan Hong Kong Baptist University Douglas Chai
Edge detection. (Trucco, Chapt 4 AND Jain et al., Chapt 5) -Edges are significant local changes of intensity in an image.
Edge detection (Trucco, Chapt 4 AND Jain et al., Chapt 5) Definition of edges -Edges are significant local changes of intensity in an image. -Edges typically occur on the boundary between two different
A NEW SUPER RESOLUTION TECHNIQUE FOR RANGE DATA. Valeria Garro, Pietro Zanuttigh, Guido M. Cortelazzo. University of Padova, Italy
A NEW SUPER RESOLUTION TECHNIQUE FOR RANGE DATA Valeria Garro, Pietro Zanuttigh, Guido M. Cortelazzo University of Padova, Italy ABSTRACT Current Time-of-Flight matrix sensors allow for the acquisition
DSP First Laboratory Exercise #9 Sampling and Zooming of Images In this lab we study the application of FIR ltering to the image zooming problem, where lowpass lters are used to do the interpolation needed
Color holographic 3D display unit with aperture field division
Color holographic 3D display unit with aperture field division Weronika Zaperty, Tomasz Kozacki, Malgorzata Kujawinska, Grzegorz Finke Photonics Engineering Division, Faculty of Mechatronics Warsaw University
Dynamic Resolution Rendering
Dynamic Resolution Rendering Doug Binks Introduction The resolution selection screen has been one of the defining aspects of PC gaming since the birth of games. In this whitepaper and the accompanying
BLENDER INTRO BLENDER TIPS
AIG-3D.ps Page 1 AIG-3D.ps Page 2 AIG-3D.ps Page 3 AIG-3D.ps Page 4 AIG-3D.ps Page 5 AIG-3D.ps Page 6 AIG-3D.ps Page 7 AIG-3D.ps Page 8
Color Image Stegananalysis Using Correlations between RGB Channels
Color Image Stegananalysis Using Correlations between RGB Channels Hasan Abdulrahman, Marc Chaumont, Philippe Montesinos, Baptiste Magnier To cite this version: Hasan Abdulrahman, Marc Chaumont, Philippe
Scanners and How to Use Them
Written by Jonathan Sachs Copyright 1996-1999 Digital Light & Color Introduction A scanner is a device that converts images to a digital file you can use with your computer. There are many different types
Scan-Line Fill. Scan-Line Algorithm. Sort by scan line Fill each span vertex order generated by vertex list
Scan-Line Fill Can also fill by maintaining a data structure of all intersections of polygons with scan lines Sort by scan line Fill each span vertex order generated by vertex list desired order Scan-Line
MassArt Studio Foundation: Visual Language Digital Media Cookbook, Fall 2013
INPUT OUTPUT 08 / IMAGE QUALITY & VIEWING In this section we will cover common image file formats you are likely to come across and examine image quality in terms of resolution and bit depth. We will cover
Introduction to Medical Imaging. Lecture 11: Cone-Beam CT Theory. Introduction. Available cone-beam reconstruction methods: Our discussion:
Introduction Introduction to Medical Imaging Lecture 11: Cone-Beam CT Theory Klaus Mueller Available cone-beam reconstruction methods: exact approximate algebraic Our discussion: exact (now) approximate
Introduction to Digital Resolution
Introduction to Digital Resolution 2011 Copyright Les Walkling 2011 Adobe Photoshop screen shots reprinted with permission from Adobe Systems Incorporated. Version 2011:02 CONTENTS Pixels of Resolution
Current status of image matching for Earth observation
Current status of image matching for Earth observation Christian Heipke IPI - Institute for Photogrammetry and GeoInformation Leibniz Universität Hannover Secretary General, ISPRS Content Introduction
Forensic Image Processing. www.martinojerian.com
Forensic Image Processing www.martinojerian.com Forensic Image Processing Lesson 1 An introduction on digital images Purpose of the course What is a digital image? What use can images have for investigative
Geometric Camera Parameters
Geometric Camera Parameters What assumptions have we made so far? -All equations we have derived for far are written in the camera reference frames. -These equations are valid only when: () all distances
White Paper. "See" what is important
Bear this in mind when selecting a book scanner "See" what is important Books, magazines and historical documents come in hugely different colors, shapes and sizes; for libraries, archives and museums,
Monash University Clayton s School of Information Technology CSE3313 Computer Graphics Sample Exam Questions 2007
Monash University Clayton s School of Information Technology CSE3313 Computer Graphics Questions 2007 INSTRUCTIONS: Answer all questions. Spend approximately 1 minute per mark. Question 1 30 Marks Total
Planetary Imaging Workshop Larry Owens
Planetary Imaging Workshop Larry Owens Lowell Observatory, 1971-1973 Backyard Telescope, 2005 How is it possible? How is it done? Lowell Observatory Sequence,1971 Acquisition E-X-P-E-R-I-M-E-N-T-A-T-I-O-N!
EECS 556 Image Processing W 09. Interpolation. Interpolation techniques B splines
EECS 556 Image Processing W 09 Interpolation Interpolation techniques B splines What is image processing? Image processing is the application of 2D signal processing methods to images Image representation
Bildverarbeitung und Mustererkennung Image Processing and Pattern Recognition
Bildverarbeitung und Mustererkennung Image Processing and Pattern Recognition 1. Image Pre-Processing - Pixel Brightness Transformation - Geometric Transformation - Image Denoising 1 1. Image Pre-Processing
COLOR-BASED PRINTED CIRCUIT BOARD SOLDER SEGMENTATION
COLOR-BASED PRINTED CIRCUIT BOARD SOLDER SEGMENTATION Tz-Sheng Peng ( 彭 志 昇 ), Chiou-Shann Fuh ( 傅 楸 善 ) Dept. of Computer Science and Information Engineering, National Taiwan University E-mail: [email protected]
Using Photorealistic RenderMan for High-Quality Direct Volume Rendering
Using Photorealistic RenderMan for High-Quality Direct Volume Rendering Cyrus Jam [email protected] Mike Bailey [email protected] San Diego Supercomputer Center University of California San Diego Abstract With
Automatic and Objective Measurement of Residual Stress and Cord in Glass
Automatic and Objective Measurement of Residual Stress and Cord in Glass GlassTrend - ICG TC15/21 Seminar SENSORS AND PROCESS CONTROL 13-14 October 2015, Eindhoven Henning Katte, ilis gmbh copyright ilis
Super-Resolution Methods for Digital Image and Video Processing
CZECH TECHNICAL UNIVERSITY IN PRAGUE FACULTY OF ELECTRICAL ENGINEERING DEPARTMENT OF RADIOELECTRONICS Super-Resolution Methods for Digital Image and Video Processing DIPLOMA THESIS Author: Bc. Tomáš Lukeš
White paper. CCD and CMOS sensor technology Technical white paper
White paper CCD and CMOS sensor technology Technical white paper Table of contents 1. Introduction to image sensors 3 2. CCD technology 4 3. CMOS technology 5 4. HDTV and megapixel sensors 6 5. Main differences
Machine Learning and Data Mining. Regression Problem. (adapted from) Prof. Alexander Ihler
Machine Learning and Data Mining Regression Problem (adapted from) Prof. Alexander Ihler Overview Regression Problem Definition and define parameters ϴ. Prediction using ϴ as parameters Measure the error
A Study on SURF Algorithm and Real-Time Tracking Objects Using Optical Flow
, pp.233-237 http://dx.doi.org/10.14257/astl.2014.51.53 A Study on SURF Algorithm and Real-Time Tracking Objects Using Optical Flow Giwoo Kim 1, Hye-Youn Lim 1 and Dae-Seong Kang 1, 1 Department of electronices
Multispectral Demosaicing using Intensity-based Spectral Correlation
Multispectral Demosaicing using Intensity-based Spectral Correlation Sofiane Mihoubi, Olivier Losson, Benjamin Mathon, Ludovic Macaire To cite this version: Sofiane Mihoubi, Olivier Losson, Benjamin Mathon,
Adobe Marketing Cloud Sharpening images in Scene7 Publishing System and on Image Server
Adobe Marketing Cloud Sharpening images in Scene7 Publishing System and on Image Server Contents Contact and Legal Information...3 About image sharpening...4 Adding an image preset to save frequently used
To determine vertical angular frequency, we need to express vertical viewing angle in terms of and. 2tan. (degree). (1 pt)
Polytechnic University, Dept. Electrical and Computer Engineering EL6123 --- Video Processing, S12 (Prof. Yao Wang) Solution to Midterm Exam Closed Book, 1 sheet of notes (double sided) allowed 1. (5 pt)
VGA video signal generation
A VGA display controller VGA video signal generation A VGA video signal contains 5 active signals: horizontal sync: digital signal, used for synchronisation of the video vertical sync: digital signal,
Joint MAP Registration and High Resolution Image Estimation Using a Sequence of Undersampled Images 1
Joint MAP Registration and High Resolution Image Estimation Using a Sequence of Undersampled Images Russell C. Hardie, Kenneth J. Barnard and Ernest E. Armstrong Department of Electrical and Computer Engineering
HSI BASED COLOUR IMAGE EQUALIZATION USING ITERATIVE n th ROOT AND n th POWER
HSI BASED COLOUR IMAGE EQUALIZATION USING ITERATIVE n th ROOT AND n th POWER Gholamreza Anbarjafari icv Group, IMS Lab, Institute of Technology, University of Tartu, Tartu 50411, Estonia [email protected]
Super-resolution method based on edge feature for high resolution imaging
Science Journal of Circuits, Systems and Signal Processing 2014; 3(6-1): 24-29 Published online December 26, 2014 (http://www.sciencepublishinggroup.com/j/cssp) doi: 10.11648/j.cssp.s.2014030601.14 ISSN:
Algorithms for the resizing of binary and grayscale images using a logical transform
Algorithms for the resizing of binary and grayscale images using a logical transform Ethan E. Danahy* a, Sos S. Agaian b, Karen A. Panetta a a Dept. of Electrical and Computer Eng., Tufts University, 161
1051-232 Imaging Systems Laboratory II. Laboratory 4: Basic Lens Design in OSLO April 2 & 4, 2002
05-232 Imaging Systems Laboratory II Laboratory 4: Basic Lens Design in OSLO April 2 & 4, 2002 Abstract: For designing the optics of an imaging system, one of the main types of tools used today is optical
Introduction to MATLAB (Basics) Reference from: Azernikov Sergei [email protected]
Introduction to MATLAB (Basics) Reference from: Azernikov Sergei [email protected] MATLAB Basics Where to get help? 1) In MATLAB s prompt type: help, lookfor,helpwin, helpdesk, demos. 2) On the
7 Lens Shading Correction for Dirt Detection
7 Lens Shading Correction for Dirt Detection Chih-Wei Chen 1 and Chiou-Shann Fuh 1 Abstract We present a novel inspection framework to detect dirt and blemish in production line of optical fabrication
Digital Systems Design. VGA Video Display Generation
Digital Systems Design Video Signal Generation for the Altera DE Board Dr. D. J. Jackson Lecture 12-1 VGA Video Display Generation A VGA signal contains 5 active signals Two TTL compatible signals for
Course Project Lab 3 - Creating a Logo (Illustrator)
Course Project Lab 3 - Creating a Logo (Illustrator) In this lab you will learn to use Adobe Illustrator to create a vector-based design logo. 1. Start Illustrator. Open the lizard.ai file via the File>Open
Blood Vessel Classification into Arteries and Veins in Retinal Images
Blood Vessel Classification into Arteries and Veins in Retinal Images Claudia Kondermann and Daniel Kondermann a and Michelle Yan b a Interdisciplinary Center for Scientific Computing (IWR), University
Understanding HD: Frame Rates, Color & Compression
Understanding HD: Frame Rates, Color & Compression HD Format Breakdown An HD Format Describes (in no particular order) Resolution Frame Rate Bit Rate Color Space Bit Depth Color Model / Color Gamut Color
How To Make A Texture Map Work Better On A Computer Graphics Card (Or Mac)
Improved Alpha-Tested Magnification for Vector Textures and Special Effects Chris Green Valve (a) 64x64 texture, alpha-blended (b) 64x64 texture, alpha tested (c) 64x64 texture using our technique Figure