To Denoise or Deblur: Parameter Optimization for Imaging Systems
|
|
- Gervais Williamson
- 6 years ago
- Views:
Transcription
1 To Denoise or Deblur: Parameter Optimization for Imaging Systems Kaushik Mitra, Oliver Cossairt and Ashok Veeraraghavan 1 ECE, Rice University 2 EECS, Northwestern University 3/3/2014 1
2 Capture moving object Short exposure Medium exposure Large exposure Increasing exposure time Noise decreases but motion blur increases
3 Recovered image Captured image Denoise or Deblur? Short exposure Large exposure Denoise Deblur
4 Effect of illumination Low illumination Denoise Deblur Strong illumination Denoise Deblur Find optimal exposure as a function of illumination
5 Depth of field Small aperture Medium aperture Large aperture Increasing aperture Noise decreases but defocus blur increases Find optimal aperture as a function of illumination 3/3/2014 5
6 A framework for analysis of computational imaging (CI) systems K. Mitra, O. Cossairt and A. Veeraraghavan, A framework for analysis of computational imaging systems and its Practical Implications, under review at IEEE PAMI, copy available in arxiv.
7 Computational imaging (CI) model Scene Image Computational camera Multiplexed image Multiplexed image Multiplexing matrix (Read+photon) Noise Image prior P(x)
8 Prior Work: Analysis of CI systems 1. Analysis under read noise without prior 2. Analysis under affine noise without prior Harwitt et al Prior Analysis Ignores Ratner et al. 2007, Wuttig 2007, Hasinoff et al. 2008, Ihrke et al. 2010, Cossairt et al the Impact of Image Priors Completely 3. Relates performance to practical considerations such as illumination, sensor characteristics, etc. Cossairt et al. 2012
9 Large exposure Short exposure Effect of image priors Captured image Recovery without Image prior Recovery with image prior
10 Our analysis framework Scene Image Computational camera Multiplexed image Multiplexed image Multiplexing matrix (Read+photon) Noise Image prior P(x) Our analysis takes into account: Image prior, P(x) Multiplexing matrix, H Noise characteristics
11 Our analysis framework: GMM as signal prior Advantages of GMM 1.Universal approximation property 3. State-of-the-art results Image processing Yu et al LF processing Sorenson et al., Analytically tractable A special case is Gaussian prior, whose MMSE can be computed analytically Mitra et al. 2012
12 Our analysis framework: Linear system Multiplexed image Multiplexing matrix Noise Motion blur Defocus blur Single pixel camera [Raskar 06] [Levin 08] [Cho 10] Light Field Capture [Hausler 72] [Nagahara 08] [Dowski, Cathey 96] [Levin et al. 07] [Zhou, Nayar 08] Reflectance [Wakin et al., 2006] High speed video [Lanman 08] [Veeraraghavan 07] [Liang 08] [Schechner 03] [Ratner 07] [Ratner 08] [Hitomi et al. 2011][Veera et al., 2011]
13 Our analysis framework: Affine noise model Noise Variance at i th Pixel: photon noise aperture, lighting, pixel size read noise electronics, ADC s, quantization J i : i th pixel intensity Photon noise signal dependent Read noise signal independent Noise PDF: Photon noise modeled as Gaussian (good approx. if #photons > 10) Slide courtesy Oliver Cossairt
14 MMSE as a performance metric Mean Squared Error (MSE) of an estimator is defined as: MMSE estimator: Defined as the estimator that achieves the minimum MSE Performance measure: SNR gain w.r.t impulse imaging : Impulse imaging system: Captures image directly -> H is identity Motion capture -> small exposure Depth of field -> small aperture
15 Practical system performance depends on 1. Illumination condition 2. Scene reflectivity 3. Camera parameters F/#, Exposure time t, quantum efficiency q, pixel size p Average signal-level is given by: Average Signal (e - ) Illumination (lux) Reflectivity Aperture Exposure Time (s) Quantum Efficiency Pixel Size (m) H depends only on camera parameters
16 Optimal exposure for capturing motion
17 Capture moving object Short exposure Medium exposure Large exposure Increasing exposure time Noise decreases but motion blur increases Find optimal exposure as a function of illumination
18 Modeling using CI framework CI framework : Captured image Multiplexing matrix Image prior P(x) (Read+photon) Noise Short exposure Medium exposure Large exposure : Identity matrix Impulse imaging system : Toeplitz matrix with medium PSF : Toeplitz matrix with large PSF Image prior P(x) is common, GMM Photon noise depends on light throughput (illumination and exposure)
19 Decoded image Captured image SNR gain (in db) Optimal PSF, Low illumination 1 lux Analytic SNR gain Vs. Blur size Exposure: t impulse =6 ms t opt = 138 ms Impulse image PSF length Other camera parameters Aperture: F/11 Pixel size: 5 µm Scene reflec: 0.5 Quantum eff: 0.5 PSF length 1 PSF length 5 PSF length 11 PSF length 17 PSF length 23 SNR = 5.9 db SNR = 9.1 db SNR = 12.2 db SNR = 13.5 db SNR=13.9 db
20 Decoded image Captured image SNR gain (in db) Optimal PSF, medium illumination 10 lux Analytic SNR gain Vs. Blur PSF t impulse =6 ms t opt = 30 ms Impulse image PSF size PSF length 1 PSF length 5 PSF length 11 PSF length 17 PSF length 23 SNR = 12.3 db SNR = 18.2 db SNR = 18.4 db SNR = 17.7 db SNR = 17.2 db
21 Decoded image Captured image SNR gain (in db) Optimal PSF, strong illumination 1000 lux Analytic SNR gain Vs. Blur PSF t impulse =6 ms t opt = 6 ms Impulse image PSF size PSF length 1 PSF length 5 PSF length 11 PSF length 17 PSF length 23 SNR = 28.4 db SNR = 25.9 db SNR = 23.6 db SNR = 22.0 db SNR = 20.1 db
22 Optimal PSF size Optimal PSF vs. illumination Optimal PSF at different light levels Illuminance (lux) For illumination > 150 lux, impulse setting is optimal Look up table for optimal exposure 3/3/
23 Optimal aperture for depth of field
24 Depth of field Increasing aperture Noise decreases but defocus blur increases Small aperture Medium aperture Large aperture Find optimal aperture as a function of illumination 3/3/
25 Modeling using CI framework CI framework : Captured image Multiplexing matrix Image prior P(x) (Read+photon) Noise Small aperture Medium aperture Large aperture : Identity matrix : Block Toeplitz matrix Impulse imaging system with medium PSF : Block Toeplitz matrix With large PSF Common image prior P(x), GMM Photon noise depends on light throughput Aperture and illumination 3/3/
26 Decoded image Captured image SNR gain (in db) Optimal PSF, low illumination 1 lux Analytic SNR gain Vs. Blur size Aperture: Impulse: f/11 Optimal: f/1.2 Impulse image PSF size Other camera parameters Exposure: 6 ms Pixel size: 5 µm Scene reflec: 0.5 Quantum eff: 0.5 PSF size 1 1 PSF size 3 3 PSF size 5 5 PSF size 7 7 PSF size 9 9 SNR=10.3 db SNR=12.0 db SNR=15.4 db SNR=17.0 db SNR=17.3 db
27 Decoded image Captured image SNR gain (in db) Optimal PSF, medium illumination 10 lux Analytic SNR gain Vs. Blur PSF Aperture: Impulse: f/11 Optimal: f/2.2 Impulse image PSF size PSF size 1 1 PSF size 3 3 PSF size 5 5 PSF size 7 7 PSF size 9 9 SNR=12.3 db SNR=19.5 db SNR=20 db SNR=19.2 db SNR=18.5 db
28 Decoded image Captured image SNR gain (in db) Optimal PSF, strong illumination 1000 lux Analytic SNR gain Vs. Blur PSF Aperture: Impulse: f/11 Optimal: f/11 Impulse image PSF size PSF size 1 1 PSF size 3 3 PSF size 5 5 PSF size 7 7 PSF size 9 9 SNR=26.6 db SNR=26.1 db SNR=23.3 db SNR=19.4 db SNR=17.4 db
29 Optimal PSF size Optimal PSF vs. illumination Optimal PSF at different light levels Illuminance (lux) For illumination > 400 lux, impulse setting is optimal Look up table for optimal aperture 3/3/
30 Optimal PSF size Conclusion Analytic framework for CI systems Project webpage: Optimal camera parameter estimation Illuminance (lux)
A Framework for Analysis of Computational Imaging Systems
A Framework for Analysis of Computational Imaging Systems Kaushik Mitra, Oliver Cossairt, Ashok Veeraghavan Rice University Northwestern University Computational imaging CI systems that adds new functionality
More informationTo Denoise or Deblur: Parameter Optimization for Imaging Systems
To Denoise or Deblur: Parameter Optimization for Imaging Systems Kaushik Mitra a, Oliver Cossairt b and Ashok Veeraraghavan a a Electrical and Computer Engineering, Rice University, Houston, TX 77005 b
More informationWhen Does Computational Imaging Improve Performance?
When Does Computational Imaging Improve Performance? Oliver Cossairt Assistant Professor Northwestern University Collaborators: Mohit Gupta, Changyin Zhou, Daniel Miau, Shree Nayar (Columbia University)
More informationA Framework for Analysis of Computational Imaging Systems: Role of Signal Prior, Sensor Noise and Multiplexing
SNR gain (in db) 1 A Framework for Analysis of Computational Imaging Systems: Role of Signal Prior, Sensor Noise and Multiplexing Kaushik Mitra, Member, IEEE, Oliver S. Cossairt, Member, IEEE and Ashok
More informationCoded Computational Photography!
Coded Computational Photography! EE367/CS448I: Computational Imaging and Display! stanford.edu/class/ee367! Lecture 9! Gordon Wetzstein! Stanford University! Coded Computational Photography - Overview!!
More informationNear-Invariant Blur for Depth and 2D Motion via Time-Varying Light Field Analysis
Near-Invariant Blur for Depth and 2D Motion via Time-Varying Light Field Analysis Yosuke Bando 1,2 Henry Holtzman 2 Ramesh Raskar 2 1 Toshiba Corporation 2 MIT Media Lab Defocus & Motion Blur PSF Depth
More informationComputational Camera & Photography: Coded Imaging
Computational Camera & Photography: Coded Imaging Camera Culture Ramesh Raskar MIT Media Lab http://cameraculture.media.mit.edu/ Image removed due to copyright restrictions. See Fig. 1, Eight major types
More informationCoding and Modulation in Cameras
Coding and Modulation in Cameras Amit Agrawal June 2010 Mitsubishi Electric Research Labs (MERL) Cambridge, MA, USA Coded Computational Imaging Agrawal, Veeraraghavan, Narasimhan & Mohan Schedule Introduction
More informationDappled Photography: Mask Enhanced Cameras for Heterodyned Light Fields and Coded Aperture Refocusing
Dappled Photography: Mask Enhanced Cameras for Heterodyned Light Fields and Coded Aperture Refocusing Ashok Veeraraghavan, Ramesh Raskar, Ankit Mohan & Jack Tumblin Amit Agrawal, Mitsubishi Electric Research
More informationCoded photography , , Computational Photography Fall 2018, Lecture 14
Coded photography http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2018, Lecture 14 Overview of today s lecture The coded photography paradigm. Dealing with
More informationDepth from Diffusion
Depth from Diffusion Changyin Zhou Oliver Cossairt Shree Nayar Columbia University Supported by ONR Optical Diffuser Optical Diffuser ~ 10 micron Micrograph of a Holographic Diffuser (RPC Photonics) [Gray,
More informationDeblurring. Basics, Problem definition and variants
Deblurring Basics, Problem definition and variants Kinds of blur Hand-shake Defocus Credit: Kenneth Josephson Motion Credit: Kenneth Josephson Kinds of blur Spatially invariant vs. Spatially varying
More informationCoded photography , , Computational Photography Fall 2017, Lecture 18
Coded photography http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2017, Lecture 18 Course announcements Homework 5 delayed for Tuesday. - You will need cameras
More informationOptimal Single Image Capture for Motion Deblurring
Optimal Single Image Capture for Motion Deblurring Amit Agrawal Mitsubishi Electric Research Labs (MERL) 1 Broadway, Cambridge, MA, USA agrawal@merl.com Ramesh Raskar MIT Media Lab Ames St., Cambridge,
More informationSimulated Programmable Apertures with Lytro
Simulated Programmable Apertures with Lytro Yangyang Yu Stanford University yyu10@stanford.edu Abstract This paper presents a simulation method using the commercial light field camera Lytro, which allows
More informationShort-course Compressive Sensing of Videos
Short-course Compressive Sensing of Videos Venue CVPR 2012, Providence, RI, USA June 16, 2012 Richard G. Baraniuk Mohit Gupta Aswin C. Sankaranarayanan Ashok Veeraraghavan Tutorial Outline Time Presenter
More informationFocal Sweep Videography with Deformable Optics
Focal Sweep Videography with Deformable Optics Daniel Miau Columbia University dmiau@cs.columbia.edu Oliver Cossairt Northwestern University ollie@eecs.northwestern.edu Shree K. Nayar Columbia University
More informationModeling and Synthesis of Aperture Effects in Cameras
Modeling and Synthesis of Aperture Effects in Cameras Douglas Lanman, Ramesh Raskar, and Gabriel Taubin Computational Aesthetics 2008 20 June, 2008 1 Outline Introduction and Related Work Modeling Vignetting
More informationCoded Exposure Deblurring: Optimized Codes for PSF Estimation and Invertibility
Coded Exposure Deblurring: Optimized Codes for PSF Estimation and Invertibility Amit Agrawal Yi Xu Mitsubishi Electric Research Labs (MERL) 201 Broadway, Cambridge, MA, USA [agrawal@merl.com,xu43@cs.purdue.edu]
More informationImage and Depth from a Single Defocused Image Using Coded Aperture Photography
Image and Depth from a Single Defocused Image Using Coded Aperture Photography Mina Masoudifar a, Hamid Reza Pourreza a a Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
More informationProject 4 Results http://www.cs.brown.edu/courses/cs129/results/proj4/jcmace/ http://www.cs.brown.edu/courses/cs129/results/proj4/damoreno/ http://www.cs.brown.edu/courses/csci1290/results/proj4/huag/
More informationComputational Photography Introduction
Computational Photography Introduction Jongmin Baek CS 478 Lecture Jan 9, 2012 Background Sales of digital cameras surpassed sales of film cameras in 2004. Digital cameras are cool Free film Instant display
More informationExtended Depth of Field Catadioptric Imaging Using Focal Sweep
Extended Depth of Field Catadioptric Imaging Using Focal Sweep Ryunosuke Yokoya Columbia University New York, NY 10027 yokoya@cs.columbia.edu Shree K. Nayar Columbia University New York, NY 10027 nayar@cs.columbia.edu
More informationKAUSHIK MITRA CURRENT POSITION. Assistant Professor at Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai.
KAUSHIK MITRA School Address Department of Electrical Engineering Indian Institute of Technology Madras Chennai, TN, India 600036 Web: www.ee.iitm.ac.in/kmitra Email: kmitra@ee.iitm.ac.in Contact: 91-44-22574411
More informationCompressive Imaging. Aswin Sankaranarayanan (Computational Photography Fall 2017)
Compressive Imaging Aswin Sankaranarayanan (Computational Photography Fall 2017) Traditional Models for Sensing Linear (for the most part) Take as many measurements as unknowns sample Traditional Models
More informationCoded Aperture Pairs for Depth from Defocus
Coded Aperture Pairs for Depth from Defocus Changyin Zhou Columbia University New York City, U.S. changyin@cs.columbia.edu Stephen Lin Microsoft Research Asia Beijing, P.R. China stevelin@microsoft.com
More informationCoded Aperture and Coded Exposure Photography
Coded Aperture and Coded Exposure Photography Martin Wilson University of Cape Town Cape Town, South Africa Email: Martin.Wilson@uct.ac.za Fred Nicolls University of Cape Town Cape Town, South Africa Email:
More informationCoded Exposure HDR Light-Field Video Recording
Coded Exposure HDR Light-Field Video Recording David C. Schedl, Clemens Birklbauer, and Oliver Bimber* Johannes Kepler University Linz *firstname.lastname@jku.at Exposure Sequence long exposed short HDR
More informationMotion-invariant Coding Using a Programmable Aperture Camera
[DOI: 10.2197/ipsjtcva.6.25] Research Paper Motion-invariant Coding Using a Programmable Aperture Camera Toshiki Sonoda 1,a) Hajime Nagahara 1,b) Rin-ichiro Taniguchi 1,c) Received: October 22, 2013, Accepted:
More informationWhat are Good Apertures for Defocus Deblurring?
What are Good Apertures for Defocus Deblurring? Changyin Zhou, Shree Nayar Abstract In recent years, with camera pixels shrinking in size, images are more likely to include defocused regions. In order
More informationTransfer Efficiency and Depth Invariance in Computational Cameras
Transfer Efficiency and Depth Invariance in Computational Cameras Jongmin Baek Stanford University IEEE International Conference on Computational Photography 2010 Jongmin Baek (Stanford University) Transfer
More informationRecent Advances in Image Deblurring. Seungyong Lee (Collaboration w/ Sunghyun Cho)
Recent Advances in Image Deblurring Seungyong Lee (Collaboration w/ Sunghyun Cho) Disclaimer Many images and figures in this course note have been copied from the papers and presentation materials of previous
More informationBurst Photography! EE367/CS448I: Computational Imaging and Display! stanford.edu/class/ee367! Lecture 7! Gordon Wetzstein! Stanford University!
Burst Photography! EE367/CS448I: Computational Imaging and Display! stanford.edu/class/ee367! Lecture 7! Gordon Wetzstein! Stanford University! Motivation! wikipedia! exposure sequence! -4 stops! Motivation!
More informationComputational Cameras. Rahul Raguram COMP
Computational Cameras Rahul Raguram COMP 790-090 What is a computational camera? Camera optics Camera sensor 3D scene Traditional camera Final image Modified optics Camera sensor Image Compute 3D scene
More informationThe ultimate camera. Computational Photography. Creating the ultimate camera. The ultimate camera. What does it do?
Computational Photography The ultimate camera What does it do? Image from Durand & Freeman s MIT Course on Computational Photography Today s reading Szeliski Chapter 9 The ultimate camera Infinite resolution
More informationAnalysis of Coded Apertures for Defocus Deblurring of HDR Images
CEIG - Spanish Computer Graphics Conference (2012) Isabel Navazo and Gustavo Patow (Editors) Analysis of Coded Apertures for Defocus Deblurring of HDR Images Luis Garcia, Lara Presa, Diego Gutierrez and
More informationDeconvolution , , Computational Photography Fall 2018, Lecture 12
Deconvolution http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2018, Lecture 12 Course announcements Homework 3 is out. - Due October 12 th. - Any questions?
More informationDeconvolution , , Computational Photography Fall 2017, Lecture 17
Deconvolution http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2017, Lecture 17 Course announcements Homework 4 is out. - Due October 26 th. - There was another
More informationNoise and ISO. CS 178, Spring Marc Levoy Computer Science Department Stanford University
Noise and ISO CS 178, Spring 2014 Marc Levoy Computer Science Department Stanford University Outline examples of camera sensor noise don t confuse it with JPEG compression artifacts probability, mean,
More informationMotion Estimation from a Single Blurred Image
Motion Estimation from a Single Blurred Image Image Restoration: De-Blurring Build a Blur Map Adapt Existing De-blurring Techniques to real blurred images Analysis, Reconstruction and 3D reconstruction
More informationImplementation of Adaptive Coded Aperture Imaging using a Digital Micro-Mirror Device for Defocus Deblurring
Implementation of Adaptive Coded Aperture Imaging using a Digital Micro-Mirror Device for Defocus Deblurring Ashill Chiranjan and Bernardt Duvenhage Defence, Peace, Safety and Security Council for Scientific
More informationExtended depth of field for visual measurement systems with depth-invariant magnification
Extended depth of field for visual measurement systems with depth-invariant magnification Yanyu Zhao a and Yufu Qu* a,b a School of Instrument Science and Opto-Electronic Engineering, Beijing University
More informationTradeoffs and Limits in Computational Imaging. Oliver Cossairt
Tradeoffs and Limits in Computational Imaging Oliver Cossairt Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA
More informationProblem Session 6. Computa(onal Imaging and Display EE 367 / CS 448I
Problem Session 6 Computa(onal Imaging and Display EE 367 / CS 448I Topics Photo- electron shot- noise SNR calcula@ons Deconvolu@on of an image with Poisson noise Wiener deconvolu@on Richardson- Lucy Richardson-
More informationPoint Spread Function Engineering for Scene Recovery. Changyin Zhou
Point Spread Function Engineering for Scene Recovery Changyin Zhou Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences
More informationSensing Increased Image Resolution Using Aperture Masks
Sensing Increased Image Resolution Using Aperture Masks Ankit Mohan, Xiang Huang, Jack Tumblin Northwestern University Ramesh Raskar MIT Media Lab CVPR 2008 Supplemental Material Contributions Achieve
More informationFlexible Depth of Field Photography
TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 Flexible Depth of Field Photography Sujit Kuthirummal, Hajime Nagahara, Changyin Zhou, and Shree K. Nayar Abstract The range of scene depths
More informationA Review over Different Blur Detection Techniques in Image Processing
A Review over Different Blur Detection Techniques in Image Processing 1 Anupama Sharma, 2 Devarshi Shukla 1 E.C.E student, 2 H.O.D, Department of electronics communication engineering, LR College of engineering
More informationGeneralized Assorted Camera Arrays: Robust Cross-channel Registration and Applications Jason Holloway, Kaushik Mitra, Sanjeev Koppal, Ashok
Generalized Assorted Camera Arrays: Robust Cross-channel Registration and Applications Jason Holloway, Kaushik Mitra, Sanjeev Koppal, Ashok Veeraraghavan Cross-modal Imaging Hyperspectral Cross-modal Imaging
More informationDefocus Map Estimation from a Single Image
Defocus Map Estimation from a Single Image Shaojie Zhuo Terence Sim School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore 117417, SINGAPOUR Abstract In this
More informationBlur and Recovery with FTVd. By: James Kerwin Zhehao Li Shaoyi Su Charles Park
Blur and Recovery with FTVd By: James Kerwin Zhehao Li Shaoyi Su Charles Park Blur and Recovery with FTVd By: James Kerwin Zhehao Li Shaoyi Su Charles Park Online: < http://cnx.org/content/col11395/1.1/
More informationCoded Aperture for Projector and Camera for Robust 3D measurement
Coded Aperture for Projector and Camera for Robust 3D measurement Yuuki Horita Yuuki Matugano Hiroki Morinaga Hiroshi Kawasaki Satoshi Ono Makoto Kimura Yasuo Takane Abstract General active 3D measurement
More informationOptimal Camera Parameters for Depth from Defocus
Optimal Camera Parameters for Depth from Defocus Fahim Mannan and Michael S. Langer School of Computer Science, McGill University Montreal, Quebec H3A E9, Canada. {fmannan, langer}@cim.mcgill.ca Abstract
More informationImproved motion invariant imaging with time varying shutter functions
Improved motion invariant imaging with time varying shutter functions Steve Webster a and Andrew Dorrell b Canon Information Systems Research, Australia (CiSRA), Thomas Holt Drive, North Ryde, Australia
More informationAdmin Deblurring & Deconvolution Different types of blur
Admin Assignment 3 due Deblurring & Deconvolution Lecture 10 Last lecture Move to Friday? Projects Come and see me Different types of blur Camera shake User moving hands Scene motion Objects in the scene
More informationFlexible Depth of Field Photography
TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 Flexible Depth of Field Photography Sujit Kuthirummal, Hajime Nagahara, Changyin Zhou, and Shree K. Nayar Abstract The range of scene depths
More informationToward Non-stationary Blind Image Deblurring: Models and Techniques
Toward Non-stationary Blind Image Deblurring: Models and Techniques Ji, Hui Department of Mathematics National University of Singapore NUS, 30-May-2017 Outline of the talk Non-stationary Image blurring
More informationIntroduction to Computer Vision
Introduction to Computer Vision CS / ECE 181B Thursday, April 1, 2004 Course Details HW #0 and HW #1 are available. Course web site http://www.ece.ucsb.edu/~manj/cs181b Syllabus, schedule, lecture notes,
More informationSimultaneous Image Formation and Motion Blur. Restoration via Multiple Capture
Simultaneous Image Formation and Motion Blur Restoration via Multiple Capture Xinqiao Liu and Abbas El Gamal Programmable Digital Camera Project Department of Electrical Engineering, Stanford University,
More information4 STUDY OF DEBLURRING TECHNIQUES FOR RESTORED MOTION BLURRED IMAGES
4 STUDY OF DEBLURRING TECHNIQUES FOR RESTORED MOTION BLURRED IMAGES Abstract: This paper attempts to undertake the study of deblurring techniques for Restored Motion Blurred Images by using: Wiener filter,
More informationChangyin Zhou. Ph.D, Computer Science, Columbia University Oct 2012
Changyin Zhou Software Engineer at Google X Google Inc. 1600 Amphitheater Parkway, Mountain View, CA 94043 E-mail: changyin@google.com URL: http://www.changyin.org Office: (917) 209-9110 Mobile: (646)
More informationAPJIMTC, Jalandhar, India. Keywords---Median filter, mean filter, adaptive filter, salt & pepper noise, Gaussian noise.
Volume 3, Issue 10, October 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A Comparative
More informationTotal Variation Blind Deconvolution: The Devil is in the Details*
Total Variation Blind Deconvolution: The Devil is in the Details* Paolo Favaro Computer Vision Group University of Bern *Joint work with Daniele Perrone Blur in pictures When we take a picture we expose
More informationCompressive Imaging: Theory and Practice
Compressive Imaging: Theory and Practice Mark Davenport Richard Baraniuk, Kevin Kelly Rice University ECE Department Digital Revolution Digital Acquisition Foundation: Shannon sampling theorem Must sample
More informationWhat will be on the midterm?
What will be on the midterm? CS 178, Spring 2014 Marc Levoy Computer Science Department Stanford University General information 2 Monday, 7-9pm, Cubberly Auditorium (School of Edu) closed book, no notes
More informationTHE depth of field (DOF) of an imaging system is the
58 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 33, NO. 1, JANUARY 2011 Flexible Depth of Field Photography Sujit Kuthirummal, Member, IEEE, Hajime Nagahara, Changyin Zhou, Student
More informationAn Analysis of Focus Sweep for Improved 2D Motion Invariance
3 IEEE Conference on Computer Vision and Pattern Recognition Workshops An Analysis of Focus Sweep for Improved D Motion Invariance Yosuke Bando TOSHIBA Corporation yosuke.bando@toshiba.co.jp Abstract Recent
More informationImage Denoising Using Statistical and Non Statistical Method
Image Denoising Using Statistical and Non Statistical Method Ms. Shefali A. Uplenchwar 1, Mrs. P. J. Suryawanshi 2, Ms. S. G. Mungale 3 1MTech, Dept. of Electronics Engineering, PCE, Maharashtra, India
More informationInternational Journal of Advancedd Research in Biology, Ecology, Science and Technology (IJARBEST)
Gaussian Blur Removal in Digital Images A.Elakkiya 1, S.V.Ramyaa 2 PG Scholars, M.E. VLSI Design, SSN College of Engineering, Rajiv Gandhi Salai, Kalavakkam 1,2 Abstract In many imaging systems, the observed
More informationLess Is More: Coded Computational Photography
Less Is More: Coded Computational Photography Ramesh Raskar Mitsubishi Electric Research Labs (MERL), Cambridge, MA, USA Abstract. Computational photography combines plentiful computing, digital sensors,
More informationCoding & Signal Processing for Holographic Data Storage. Vijayakumar Bhagavatula
Coding & Signal Processing for Holographic Data Storage Vijayakumar Bhagavatula Acknowledgements Venkatesh Vadde Mehmet Keskinoz Sheida Nabavi Lakshmi Ramamoorthy Kevin Curtis, Adrian Hill & Mark Ayres
More informationAcquisition. Some slides from: Yung-Yu Chuang (DigiVfx) Jan Neumann, Pat Hanrahan, Alexei Efros
Acquisition Some slides from: Yung-Yu Chuang (DigiVfx) Jan Neumann, Pat Hanrahan, Alexei Efros Image Acquisition Digital Camera Film Outline Pinhole camera Lens Lens aberrations Exposure Sensors Noise
More informationComputational Photography
Computational photography Computational Photography Digital Visual Effects Yung-Yu Chuang wikipedia: Computational photography h refers broadly to computational imaging techniques that enhance or extend
More information1. INTRODUCTION. Appeared in: Proceedings of the SPIE Biometric Technology for Human Identification II, Vol. 5779, pp , Orlando, FL, 2005.
Appeared in: Proceedings of the SPIE Biometric Technology for Human Identification II, Vol. 5779, pp. 41-50, Orlando, FL, 2005. Extended depth-of-field iris recognition system for a workstation environment
More informationRaskar, Camera Culture, MIT Media Lab. Ramesh Raskar. Camera Culture. Associate Professor, MIT Media Lab
Raskar, Camera Culture, MIT Media Lab Camera Culture Ramesh Raskar C C lt Camera Culture Associate Professor, MIT Media Lab Where are the camera s? Where are the camera s? We focus on creating tools to
More informationDemosaicing and Denoising on Simulated Light Field Images
Demosaicing and Denoising on Simulated Light Field Images Trisha Lian Stanford University tlian@stanford.edu Kyle Chiang Stanford University kchiang@stanford.edu Abstract Light field cameras use an array
More informationStochastic Image Denoising using Minimum Mean Squared Error (Wiener) Filtering
Stochastic Image Denoising using Minimum Mean Squared Error (Wiener) Filtering L. Sahawneh, B. Carroll, Electrical and Computer Engineering, ECEN 670 Project, BYU Abstract Digital images and video used
More informationAntennas and Propagation. Chapter 6d: Diversity Techniques and Spatial Multiplexing
Antennas and Propagation d: Diversity Techniques and Spatial Multiplexing Introduction: Diversity Diversity Use (or introduce) redundancy in the communications system Improve (short time) link reliability
More informationA Comprehensive Review on Image Restoration Techniques
International Journal of Research in Advent Technology, Vol., No.3, March 014 E-ISSN: 31-9637 A Comprehensive Review on Image Restoration Techniques Biswa Ranjan Mohapatra, Ansuman Mishra, Sarat Kumar
More informationLecture 30: Image Sensors (Cont) Computer Graphics and Imaging UC Berkeley CS184/284A
Lecture 30: Image Sensors (Cont) Computer Graphics and Imaging UC Berkeley Reminder: The Pixel Stack Microlens array Color Filter Anti-Reflection Coating Stack height 4um is typical Pixel size 2um is typical
More informationDigital Image Processing Labs DENOISING IMAGES
Digital Image Processing Labs DENOISING IMAGES All electronic devices are subject to noise pixels that, for one reason or another, take on an incorrect color or intensity. This is partly due to the changes
More informationLecture 3: Linear Filters
Signal Denoising Lecture 3: Linear Filters Math 490 Prof. Todd Wittman The Citadel Suppose we have a noisy 1D signal f(x). For example, it could represent a company's stock price over time. In order to
More informationPattern Recognition 44 (2011) Contents lists available at ScienceDirect. Pattern Recognition. journal homepage:
Pattern Recognition 44 () 85 858 Contents lists available at ScienceDirect Pattern Recognition journal homepage: www.elsevier.com/locate/pr Defocus map estimation from a single image Shaojie Zhuo, Terence
More informationA Kalman-Filtering Approach to High Dynamic Range Imaging for Measurement Applications
A Kalman-Filtering Approach to High Dynamic Range Imaging for Measurement Applications IEEE Transactions on Image Processing, Vol. 21, No. 2, 2012 Eric Dedrick and Daniel Lau, Presented by Ran Shu School
More informationComputer Vision. The Pinhole Camera Model
Computer Vision The Pinhole Camera Model Filippo Bergamasco (filippo.bergamasco@unive.it) http://www.dais.unive.it/~bergamasco DAIS, Ca Foscari University of Venice Academic year 2017/2018 Imaging device
More informationImage Restoration using Modified Lucy Richardson Algorithm in the Presence of Gaussian and Motion Blur
Advance in Electronic and Electric Engineering. ISSN 2231-1297, Volume 3, Number 8 (2013), pp. 1063-1070 Research India Publications http://www.ripublication.com/aeee.htm Image Restoration using Modified
More informationTRUESENSE SPARSE COLOR FILTER PATTERN OVERVIEW SEPTEMBER 30, 2013 APPLICATION NOTE REVISION 1.0
TRUESENSE SPARSE COLOR FILTER PATTERN OVERVIEW SEPTEMBER 30, 2013 APPLICATION NOTE REVISION 1.0 TABLE OF CONTENTS Overview... 3 Color Filter Patterns... 3 Bayer CFA... 3 Sparse CFA... 3 Image Processing...
More informationWavefront coding. Refocusing & Light Fields. Wavefront coding. Final projects. Is depth of field a blur? Frédo Durand Bill Freeman MIT - EECS
6.098 Digital and Computational Photography 6.882 Advanced Computational Photography Final projects Send your slides by noon on Thrusday. Send final report Refocusing & Light Fields Frédo Durand Bill Freeman
More informationResolving Objects at Higher Resolution from a Single Motion-blurred Image
MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Resolving Objects at Higher Resolution from a Single Motion-blurred Image Amit Agrawal, Ramesh Raskar TR2007-036 July 2007 Abstract Motion
More informationALMALENCE SUPER SENSOR. A software component with an effect of increasing the pixel size and number of pixels in the sensor
ALMALENCE SUPER SENSOR A software component with an effect of increasing the pixel size and number of pixels in the sensor MOBILE CAMERA: SMALL SENSOR AND TINY LENS Insufficient resolution, low light performance,
More informationImage acquisition. In both cases, the digital sensing element is one of the following: Line array Area array. Single sensor
Image acquisition Digital images are acquired by direct digital acquisition (digital still/video cameras), or scanning material acquired as analog signals (slides, photographs, etc.). In both cases, the
More informationWide Field-of-View Fluorescence Imaging of Coral Reefs
Wide Field-of-View Fluorescence Imaging of Coral Reefs Tali Treibitz, Benjamin P. Neal, David I. Kline, Oscar Beijbom, Paul L. D. Roberts, B. Greg Mitchell & David Kriegman Supplementary Note 1: Image
More informationReinterpretable Imager: Towards Variable Post-Capture Space, Angle and Time Resolution in Photography
Reinterpretable Imager: Towards Variable Post-Capture Space, Angle and Time Resolution in Photography The MIT Faculty has made this article openly available. Please share how this access benefits you.
More informationCvision 2. António J. R. Neves João Paulo Silva Cunha. Bernardo Cunha. IEETA / Universidade de Aveiro
Cvision 2 Digital Imaging António J. R. Neves (an@ua.pt) & João Paulo Silva Cunha & Bernardo Cunha IEETA / Universidade de Aveiro Outline Image sensors Camera calibration Sampling and quantization Data
More informationRecent advances in deblurring and image stabilization. Michal Šorel Academy of Sciences of the Czech Republic
Recent advances in deblurring and image stabilization Michal Šorel Academy of Sciences of the Czech Republic Camera shake stabilization Alternative to OIS (optical image stabilization) systems Should work
More informationImproving Film-Like Photography. aka, Epsilon Photography
Improving Film-Like Photography aka, Epsilon Photography Ankit Mohan Courtesy of Ankit Mohan. Used with permission. Film-like like Optics: Imaging Intuition Angle(θ,ϕ) Ray Center of Projection Position
More informationPerformance Evaluation of Different Depth From Defocus (DFD) Techniques
Please verify that () all pages are present, () all figures are acceptable, (3) all fonts and special characters are correct, and () all text and figures fit within the Performance Evaluation of Different
More informationThe Flutter Shutter Camera Simulator
2014/07/01 v0.5 IPOL article class Published in Image Processing On Line on 2012 10 17. Submitted on 2012 00 00, accepted on 2012 00 00. ISSN 2105 1232 c 2012 IPOL & the authors CC BY NC SA This article
More informationA No Reference Image Blur Detection using CPBD Metric and Deblurring of Gaussian Blurred Images using Lucy-Richardson Algorithm
A No Reference Image Blur Detection using CPBD Metric and Deblurring of Gaussian Blurred Images using Lucy-Richardson Algorithm Suresh S. Zadage, G. U. Kharat Abstract This paper addresses sharpness of
More informationAnalysis of Quality Measurement Parameters of Deblurred Images
Analysis of Quality Measurement Parameters of Deblurred Images Dejee Singh 1, R. K. Sahu 2 PG Student (Communication), Department of ET&T, Chhatrapati Shivaji Institute of Technology, Durg, India 1 Associate
More information