Coded Computational Photography!

Similar documents
Coded photography , , Computational Photography Fall 2018, Lecture 14

Coded photography , , Computational Photography Fall 2017, Lecture 18

Deblurring. Basics, Problem definition and variants

Near-Invariant Blur for Depth and 2D Motion via Time-Varying Light Field Analysis

Dappled Photography: Mask Enhanced Cameras for Heterodyned Light Fields and Coded Aperture Refocusing

Computational Camera & Photography: Coded Imaging

Coding and Modulation in Cameras

When Does Computational Imaging Improve Performance?

To Do. Advanced Computer Graphics. Outline. Computational Imaging. How do we see the world? Pinhole camera

A Framework for Analysis of Computational Imaging Systems


Optimal Single Image Capture for Motion Deblurring

Deconvolution , , Computational Photography Fall 2017, Lecture 17

Improved motion invariant imaging with time varying shutter functions

Coded Aperture for Projector and Camera for Robust 3D measurement

Deconvolution , , Computational Photography Fall 2018, Lecture 12

Transfer Efficiency and Depth Invariance in Computational Cameras

Focal Sweep Videography with Deformable Optics

Coded Exposure Deblurring: Optimized Codes for PSF Estimation and Invertibility

Computational Cameras. Rahul Raguram COMP

To Denoise or Deblur: Parameter Optimization for Imaging Systems

Implementation of Adaptive Coded Aperture Imaging using a Digital Micro-Mirror Device for Defocus Deblurring

Coded Aperture and Coded Exposure Photography

Computational Approaches to Cameras

Wavefront coding. Refocusing & Light Fields. Wavefront coding. Final projects. Is depth of field a blur? Frédo Durand Bill Freeman MIT - EECS

Motion-invariant Coding Using a Programmable Aperture Camera

Admin Deblurring & Deconvolution Different types of blur

What are Good Apertures for Defocus Deblurring?

A Review over Different Blur Detection Techniques in Image Processing

Extended Depth of Field Catadioptric Imaging Using Focal Sweep

Extended depth of field for visual measurement systems with depth-invariant magnification

Simulated Programmable Apertures with Lytro

Burst Photography! EE367/CS448I: Computational Imaging and Display! stanford.edu/class/ee367! Lecture 7! Gordon Wetzstein! Stanford University!

The ultimate camera. Computational Photography. Creating the ultimate camera. The ultimate camera. What does it do?

To Denoise or Deblur: Parameter Optimization for Imaging Systems

An Analysis of Focus Sweep for Improved 2D Motion Invariance

Lenses, exposure, and (de)focus

Coded Aperture Pairs for Depth from Defocus

Computational Photography Introduction

Less Is More: Coded Computational Photography

Computational Photography

Flexible Depth of Field Photography

Point Spread Function Engineering for Scene Recovery. Changyin Zhou

Implementation of Image Deblurring Techniques in Java

Toward Non-stationary Blind Image Deblurring: Models and Techniques

multiframe visual-inertial blur estimation and removal for unmodified smartphones

A Recognition of License Plate Images from Fast Moving Vehicles Using Blur Kernel Estimation

Blur and Recovery with FTVd. By: James Kerwin Zhehao Li Shaoyi Su Charles Park

Image Deblurring with Blurred/Noisy Image Pairs

4 STUDY OF DEBLURRING TECHNIQUES FOR RESTORED MOTION BLURRED IMAGES

Recent Advances in Image Deblurring. Seungyong Lee (Collaboration w/ Sunghyun Cho)

Raskar, Camera Culture, MIT Media Lab. Ramesh Raskar. Camera Culture. Associate Professor, MIT Media Lab

Head Mounted Display Optics II!

Flexible Depth of Field Photography

Computational Photography: Principles and Practice

Dappled Photography: Mask Enhanced Cameras for Heterodyned Light Fields and Coded Aperture Refocusing

Changyin Zhou. Ph.D, Computer Science, Columbia University Oct 2012

THE depth of field (DOF) of an imaging system is the

Introduction to Light Fields

Admin. Lightfields. Overview. Overview 5/13/2008. Idea. Projects due by the end of today. Lecture 13. Lightfield representation of a scene

Motion Estimation from a Single Blurred Image

Analysis of Coded Apertures for Defocus Deblurring of HDR Images

Agenda. Fusion and Reconstruction. Image Fusion & Reconstruction. Image Fusion & Reconstruction. Dr. Yossi Rubner.

Resolving Objects at Higher Resolution from a Single Motion-blurred Image

fast blur removal for wearable QR code scanners

Focal Sweep Imaging with Multi-focal Diffractive Optics

On the Recovery of Depth from a Single Defocused Image

High dynamic range imaging and tonemapping

Recent advances in deblurring and image stabilization. Michal Šorel Academy of Sciences of the Czech Republic

Defocus Map Estimation from a Single Image

Image and Depth from a Single Defocused Image Using Coded Aperture Photography

Supplementary Information

A Framework for Analysis of Computational Imaging Systems: Role of Signal Prior, Sensor Noise and Multiplexing

High resolution extended depth of field microscopy using wavefront coding

Ultra-shallow DoF imaging using faced paraboloidal mirrors

Full Resolution Lightfield Rendering

Restoration of Motion Blurred Document Images

Modeling the calibration pipeline of the Lytro camera for high quality light-field image reconstruction

Removal of Glare Caused by Water Droplets

Capturing Light. The Light Field. Grayscale Snapshot 12/1/16. P(q, f)

Coded Aperture Flow. Anita Sellent and Paolo Favaro

Lecture 18: Light field cameras. (plenoptic cameras) Visual Computing Systems CMU , Fall 2013

Motion Deblurring using Coded Exposure for a Wheeled Mobile Robot Kibaek Park, Seunghak Shin, Hae-Gon Jeon, Joon-Young Lee and In So Kweon

A Mathematical model for the determination of distance of an object in a 2D image

NTU CSIE. Advisor: Wu Ja Ling, Ph.D.

Motion Blurred Image Restoration based on Super-resolution Method

Modeling and Synthesis of Aperture Effects in Cameras

2015, IJARCSSE All Rights Reserved Page 312

The Flutter Shutter Camera Simulator

4D Frequency Analysis of Computational Cameras for Depth of Field Extension

Depth from Diffusion

Optical transfer function shaping and depth of focus by using a phase only filter

CS354 Computer Graphics Computational Photography. Qixing Huang April 23 th 2018

Total Variation Blind Deconvolution: The Devil is in the Details*

Cameras. Steve Rotenberg CSE168: Rendering Algorithms UCSD, Spring 2017

Non-Uniform Motion Blur For Face Recognition

CVPR Easter School. Michael S. Brown. School of Computing National University of Singapore

Reinterpretable Imager: Towards Variable Post-Capture Space, Angle and Time Resolution in Photography

Lecture 22: Cameras & Lenses III. Computer Graphics and Imaging UC Berkeley CS184/284A, Spring 2017

2D Barcode Localization and Motion Deblurring Using a Flutter Shutter Camera

Transcription:

Coded Computational Photography! EE367/CS448I: Computational Imaging and Display! stanford.edu/class/ee367! Lecture 9! Gordon Wetzstein! Stanford University!

Coded Computational Photography - Overview!! coded apertures!! extended depth of field!! wavefront coding!! lattice lens!! diffusion coding!! focal sweep!! motion deblurring!! flutter shutter!! motion invariance! [Raskar et al. 2006]! [Cossairt et al., 2010]!

Remember Apertures?!! out of focus blur! focal plane! circle of confusion!

What makes Defocus Deblurring Hard?! 1.! depth-dependent PSF scale (depth unknown)! 2.! circular / Airy PSF is not (well) invertible! focal plane! circle of confusion!

Coded Computational Imaging - Motivation! 1. depth-dependent PSF scale (depth unknown)! engineer PSF to be depth invariant! resulting shift-invariant deconvolution is much easier!! 2. circular / Airy PSF is not (well) invertible: ill-posed problem! engineer PSF to be broadband (flat Fourier magnitudes)! resulting inverse problem becomes well-posed!

Computational Imaging! 1.! optically encode scene information! 2.! computationally recover information!?!!!! new optics! new sensors! new illumination! new algorithms!???

Coded Computational Imaging (for this Class)! 1.! optically encode scene information using! new optics! invertible (and possibly invariant) PSF!! easier algorithms! 2.! computationally recover information (easy because of engineered PSF)!??

Coded Computational Imaging (for this Class)! idea applies to!! new optics!! coded apertures!! easier algorithms!! extended depth of field / DOF deblurring!! extended motion / motion deblurring!??

Before going to Advanced Techniques for DOF Deblurring, let s take a look at! Coded Apertures!

! two important parts:! Apertures Revisited! 1.! aperture stop attenuating pattern! 2.! refractive element (lens or compound lens system)! 1. attenuating coded aperture: e.g., MURA pattern! 2. refractive coded! aperture: e.g., cubic phase plate!

Coded Aperture Changes PSF! [Veeraragharavan et al. 2007]! in-focus photo! out-of-focus, circular aperture! out-of-focus, coded aperture!

Coded Aperture Changes PSF! [Veeraragharavan et al. 2007]! in-focus photo! out-of-focus, circular aperture! out-of-focus, coded aperture!

Coded Aperture Changes PSF! [Veeraragharavan et al. 2007]!! preserves high frequencies!! deconvolution well-posed! conventional! FFT! coded!

Coded Aperture Allows for Depth Estimation!! introduce zeros in Fourier domain!! better depth discimination!! worse invertibility! conventional aperture! coded aperture! PSF! [Levin et al. 2007]!

Coded Aperture Allows for Depth Estimation!! deconvolution with strong prior necessary! input! local depth estimate! regularized depth! [Levin et al. 2007]!

In Astronomy!! some wavelengths are difficult to focus!! no lenses available!! coded apertures for x-rays and gamma rays! ESA SPI / INTEGRAL! NASA Swift!

In Microscopy!! for low-light, coding of refraction is better (less light loss)! e.g., rotating double helix PSF Stanford Moerner lab! e.g., cubic phase plate for depth-invariant imaging!

Extended Depth of Field!

Depth Invariant PSFs - Overview!! two general approaches:! 1.! move sensor / object! (known as focal sweep)! 2. change optics! (e.g., wavefront coding)!

Focal Sweep! exposure! linear motion:! distance! sensor-lens! time! nonlinear motion:! distance! sensor-lens! time! nonlinear motion:! distance! sensor-lens! [Nagahara et al. 2008]! time!

Focal Sweep! [Nagahara et al. 2008]! distance! sensor-lens! time! time! two points at different distance!

Focal Sweep! [Nagahara et al. 2008]! PSF 1!! t 1 t 2 distance! sensor-lens! PSF 2!! time! instantaneous PSF! integrated PSF! time! two points at different distance!

Focal Sweep! [Nagahara et al. 2008]! PSF 1!! t 1 t 2 distance! sensor-lens! PSF 2!! time! instantaneous PSF! time! two points at different distance!

Focal Sweep! [Nagahara et al. 2008]! PSF 1!! t 1 t 2 t 3 distance! sensor-lens! PSF 2!! time! instantaneous PSF! integrated PSF! time! two points at different distance!

Focal Sweep! [Nagahara et al. 2008]! PSF 1!! t 1 t 2 t 3 t 4 distance! sensor-lens! time! PSF 2!! instantaneous PSF! time! two points at different distance!

Focal Sweep! [Nagahara et al. 2008]! PSF 1! PSF 2! t! 1 t 2 t 3 t 4 t 5 dt =! dt =! distance! sensor-lens! time! instantaneous PSF! integrated PSF! time! two points at different distance!

Focal Sweep! [Nagahara et al. 2008]! PSF 1! PSF 2! t! 1 t 2 t 3 t 4 t 5 dt =! dt =! distance! sensor-lens! time! instantaneous PSF! integrated PSF! time! two points at different distance!

! Focal Sweep! [Nagahara et al. 2008]!! spend equal amount of time at each depth to make depth invariant!!! distance! sensor-lens! time! integrated PSF! time! two points at different distance!

Focal Sweep! [Nagahara et al. 2008]! conventional photo (small DOF)! conventional photo (large DOF, noisy)! captured focal sweep! always blurry!! EDOF image!

! Focal Sweep!! noise characteristics are main! benefit of EDOF! may change for different sensor EDOF image! noise characteristics! [Nagahara et al. 2008]! SNR should be! evaluation metric! conventional photo (large DOF, noisy)!

Focal Sweep for Moving Objects! motion! motion! defocus! conventional camera PSF! focal sweep camera PSF! [Bando et al. 2013]!

Focal Sweep for Moving Objects! motion! motion! defocus! conventional camera PSF! focal sweep camera PSF! [Bando et al. 2013]!

Focal Sweep for Moving Objects! motion! motion! defocus! conventional camera PSF! focal sweep camera PSF! [Bando et al. 2013]!

Focal Sweep for Moving Objects! conventional camera! focal sweep! focal sweep deblurred! [Bando et al. 2013]!

! Wavefront Coding! [Dowski and Cathey 1995]!! how to obtain a depth invariant PSF without mechanically moving parts!! change the lens!! for many, this is the dawn of computational imaging! cubic phase plate!! tricky to understand intuitively, so let s try to understand what it does by looking at something else!

Lattice Focal Lens! superimpose array of lenses with different focal lengths! time! [Levin et al. 2009]!

Lattice Focal Lens! conventional camera! lattice focal lens! all-in-focus image from lattice focal lens! [Levin et al. 2009]!

Extended Depth of Field (EDOF)! remember focal sweep: move sensor s.t. same time for each depth! lattice focal lens: same idea, but no sweeping (optical overlay) optimal in 4D! cubic phase plate: same idea (optimal in 2D, not optimal in 4D)! (can look at this in more detail if we have time)!

Diffusion Coded Photography!! can also do EDOF with diffuser as coded aperture, has better inversion! characteristics than lattice focal lens! [Cossairt et al. 2010]!

Back to Coding Motion!

Flutter Shutter! [Raskar et al. 2006]! engineer motion PSF (coding exposure time) so it becomes invertible!!

photo with coded motion! [Raskar et al. 2006]!

deblurred!

[Raskar et al. 2006]! Input Photo! Deblurred Result!

! Traditional Camera! Shutter is OPEN! [Raskar et al. 2006]!

[Raskar et al. 2006]!! Flutter Shutter!

!! [Raskar et al. 2006]! Shutter is OPEN and CLOSED!

Harold Doc Edgerton H

[Raskar et al. 2006]!

Lab Setup [Raskar et al. 2006]!

[Raskar et al. 2006]! spatial convolution! sinc Function! Blurring! =! Convolution! Fourier magnitudes! Traditional Camera: Box Filter!

[Raskar et al. 2006]! spatial convolution! Preserves High Frequencies!!!! Fourier magnitudes! Flutter Shutter: Coded Filter!

Comparison! [Raskar et al. 2006]!

[Raskar et al. 2006]! Inverse Filter stable! Inverse Filter Unstable!

Short Exposure Long Exposure Coded Exposure Our result Matlab Richardson-Lucy Ground Truth

Our Code! Are all codes good?! [Raskar et al. 2006]! All ones! Alternate! Random!

License Plate Retrieval! [Raskar et al. 2006]!

License Plate Retrieval! [Raskar et al. 2006]!

! Motion Invariant Photography! making motion PSFs invariant is great, BUT need to know motion direction and velocity!! we have already seen that focal sweep makes the PSF almost depth invariant! how about making motion PSFs motion invariant?!

title!! text! Jacques Henri Lartigue, 1912!

text! animation by largeformatphotography.info user Lindolfi!

Controlling Motion Blur! [Levin et al. 2008]!

Controlling Motion Blur! [Levin et al. 2008]! Can we control motion blur?!

Controlling Motion Blur! [Levin et al. 2008]!

Controlling Motion Blur! [Levin et al. 2008]!

Controlling Motion Blur! [Levin et al. 2008]! Motion invariant blur?!

!! Sensor position x(t)=a t 2! start by moving very fast to the right! continuously slow down until stop! continuously accelerate to the left! Intuition:! for any velocity, there is one instant where we track perfectly! all velocities captured same amount of time! Parabolic Sweep! Time t! [Levin et al. 2008]! Sensor position x!

Motion Invariant Blur! [Levin et al. 2008]!

!!! [Levin et al. 2008]! Static camera! Unknown and variable blur kernels! Our parabolic input! Blur kernel is invariant to velocity! Our output after deblurring! NON-BLIND deconvolution!

t! Primal Domain! Frequency Domain! Frequency Domain!! t [Levin et al. 2008]! Objects!! x x! sensor integration! Camera integration curve! t! Parabolic sweep! x!! t Velocity 1!! x Static! Velocity 2! Equal high response in all range!

Next: Noise!!!! Gaussian noise! Poissonian noise! Denoising!

References and Further Reading! Extended Depth of Field (EDOF)! DOWSKI, E. R., AND CATHEY, W. T. 1995. Extended depth of field through wave-front coding. Appl. Opt. 34, 11, 1859 1866! Levin, Hasinoff, Green, Durand, Freeman, 4D Frequency Analysis of Computational Cameras for Depth of Field Extension, ACM SIGGRAPH 2009! Cossairt, Zhou, Nayar, Diffusion-Coded Photography, ACM SIGGRAPH 2012! overview and analysis in light field space: Zhang, Levoy, Wigner Distributions and How They Relate to the Light Field, ICCP 2009! A. Isaksen, L. McMillan, and S. J. Gortler. Dynamically reparameterized light fields. In Proc. ACM SIGGRAPH, 2000! EDOF through Focal Sweep! HAUSLER, G. 1972. A method to increase the depth of focus by two step image processing. Optics Communications 6 (Sep), 38 42.! NAGAHARA, H., KUTHIRUMMAL, S., ZHOU, C., AND NAYAR, S. 2008. Flexible Depth of Field Photography. In ECCV 08, 73! Cossairt, Nayar Spectral Focal Sweep for Extending Depth of Field, Proc. ICCP 2010! Coded Apertures! LEVIN, A., FERGUS, R., DURAND, F., AND FREEMAN, W. T. 2007. Image and depth from a conventional camera with a coded aperture. In SIGGRAPH 07, 70.! VEERARAGHAVAN, A., RASKAR, R., AGRAWAL, A., MOHAN, A., AND TUMBLIN, J. 2007. Dappled photography: mask enhanced cameras for heterodyned light fields and coded aperture refocusing. In SIGGRAPH 07, 69! ZHOU, C., AND NAYAR, S. 2009. What are Good Apertures for Defocus Deblurring? In ICCP 09! Coding Motion! Raskar, Agrawal, Tumblin, Coded Exposure Photography: Motion Deblurring using Fluttered Shutter, ACM SIGGRAPH 2006! Levin, Sand, Cho, Durand, Freeman, Motion-Invariant Photography, ACM SIGGRAPH 2008! Motion and Depth Invariance! Bando, Holtzman, Raskar, Near-Invariant Blur for Depth and 2D Motion via Time-Varying Light Field Analysis, ACM Trans. Graph. 2013! Bando, An Analysis of Focus Sweep for Improved 2D Motion Invariance, IEEE CVPR CCD Workshop 2013!!