Image Formation and Camera Design

Similar documents
Digital Imaging Rochester Institute of Technology

IMAGE FORMATION. Light source properties. Sensor characteristics Surface. Surface reflectance properties. Optics

Camera Simulation. References. Photography, B. London and J. Upton Optics in Photography, R. Kingslake The Camera, The Negative, The Print, A.

Lenses, exposure, and (de)focus

Image Formation and Capture. Acknowledgment: some figures by B. Curless, E. Hecht, W.J. Smith, B.K.P. Horn, and A. Theuwissen

Acquisition. Some slides from: Yung-Yu Chuang (DigiVfx) Jan Neumann, Pat Hanrahan, Alexei Efros

Image Formation and Capture

Chapters 1 & 2. Definitions and applications Conceptual basis of photogrammetric processing

Image Formation. Light from distant things. Geometrical optics. Pinhole camera. Chapter 36

Astronomical Cameras

Dr F. Cuzzolin 1. September 29, 2015

INTRODUCTION THIN LENSES. Introduction. given by the paraxial refraction equation derived last lecture: Thin lenses (19.1) = 1. Double-lens systems

LENSES. INEL 6088 Computer Vision

Algebra Based Physics. Reflection. Slide 1 / 66 Slide 2 / 66. Slide 3 / 66. Slide 4 / 66. Slide 5 / 66. Slide 6 / 66.

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

Chapter 18 Optical Elements

PHYS 202 OUTLINE FOR PART III LIGHT & OPTICS

Geometric optics & aberrations

VC 11/12 T2 Image Formation

Algebra Based Physics. Reflection. Slide 1 / 66 Slide 2 / 66. Slide 3 / 66. Slide 4 / 66. Slide 5 / 66. Slide 6 / 66.

CS 443: Imaging and Multimedia Cameras and Lenses

Cameras. CSE 455, Winter 2010 January 25, 2010

30 Lenses. Lenses change the paths of light.

Colorado School of Mines. Computer Vision. Professor William Hoff Dept of Electrical Engineering &Computer Science.

TSBB09 Image Sensors 2018-HT2. Image Formation Part 1

Reflectors vs. Refractors

Laboratory experiment aberrations

Applied Optics. , Physics Department (Room #36-401) , ,

Average: Standard Deviation: Max: 99 Min: 40

Applications of Optics

VC 14/15 TP2 Image Formation

IMAGE SENSOR SOLUTIONS. KAC-96-1/5" Lens Kit. KODAK KAC-96-1/5" Lens Kit. for use with the KODAK CMOS Image Sensors. November 2004 Revision 2

Big League Cryogenics and Vacuum The LHC at CERN

Chapter 36. Image Formation

ECEG105/ECEU646 Optics for Engineers Course Notes Part 4: Apertures, Aberrations Prof. Charles A. DiMarzio Northeastern University Fall 2008

Introduction. Related Work

OPTICAL SYSTEMS OBJECTIVES

Lecture 2: Geometrical Optics. Geometrical Approximation. Lenses. Mirrors. Optical Systems. Images and Pupils. Aberrations.

Image Formation: Camera Model

Chapter 26. The Refraction of Light: Lenses and Optical Instruments

Chapter 29/30. Wave Fronts and Rays. Refraction of Sound. Dispersion in a Prism. Index of Refraction. Refraction and Lenses


GEOMETRICAL OPTICS AND OPTICAL DESIGN

Cameras, lenses, and sensors

Lecture 26. PHY 112: Light, Color and Vision. Finalities. Final: Thursday May 19, 2:15 to 4:45 pm. Prof. Clark McGrew Physics D 134

Waves & Oscillations

EE119 Introduction to Optical Engineering Spring 2003 Final Exam. Name:

Lecture 2: Geometrical Optics. Geometrical Approximation. Lenses. Mirrors. Optical Systems. Images and Pupils. Aberrations.

Chapter 36. Image Formation

Astronomy 80 B: Light. Lecture 9: curved mirrors, lenses, aberrations 29 April 2003 Jerry Nelson

Cameras, lenses and sensors

Optical System Design

Warren J. Smith Chief Scientist, Consultant Rockwell Collins Optronics Carlsbad, California

Digital camera. Sensor. Memory card. Circuit board

Overview. Pinhole camera model Projective geometry Vanishing points and lines Projection matrix Cameras with Lenses Color Digital image

Name. Light Chapter Summary Cont d. Refraction

Optical Systems: Pinhole Camera Pinhole camera: simple hole in a box: Called Camera Obscura Aristotle discussed, Al-Hazen analyzed in Book of Optics

CSE 473/573 Computer Vision and Image Processing (CVIP)

c v n = n r Sin n c = n i Refraction of Light Index of Refraction Snell s Law or Refraction Example Problem Total Internal Reflection Optics

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

Lecture 9. Lecture 9. t (min)

Ch 24. Geometric Optics

Lecture 4: Geometrical Optics 2. Optical Systems. Images and Pupils. Rays. Wavefronts. Aberrations. Outline

Lens Principal and Nodal Points

Virtual and Digital Cameras

AP Physics Problems -- Waves and Light

Study on Imaging Quality of Water Ball Lens

General Imaging System

EE-527: MicroFabrication

Light. Path of Light. Looking at things. Depth and Distance. Getting light to imager. CS559 Lecture 2 Lights, Cameras, Eyes

Limitations of lenses

Performance Factors. Technical Assistance. Fundamental Optics

Lecture PowerPoint. Chapter 25 Physics: Principles with Applications, 6 th edition Giancoli

Chapter 34 Geometric Optics (also known as Ray Optics) by C.-R. Hu

R 1 R 2 R 3. t 1 t 2. n 1 n 2

Microscope anatomy, image formation and resolution

Chapter 25 Optical Instruments

Basic principles of photography. David Capel 346B IST

EE119 Introduction to Optical Engineering Spring 2002 Final Exam. Name:

Physics 1230: Light and Color. Guest Lecture, Jack again. Lecture 23: More about cameras

ABC Math Student Copy. N. May ABC Math Student Copy. Physics Week 13(Sem. 2) Name. Light Chapter Summary Cont d 2

Building a Real Camera. Slides Credit: Svetlana Lazebnik

Chapter Ray and Wave Optics

Reflection! Reflection and Virtual Image!

Point Spread Function. Confocal Laser Scanning Microscopy. Confocal Aperture. Optical aberrations. Alternative Scanning Microscopy

Lecture Notes 10 Image Sensor Optics. Imaging optics. Pixel optics. Microlens

Opti 415/515. Introduction to Optical Systems. Copyright 2009, William P. Kuhn

Image Formation. Dr. Gerhard Roth. COMP 4102A Winter 2015 Version 3

PHYSICS. Chapter 35 Lecture FOR SCIENTISTS AND ENGINEERS A STRATEGIC APPROACH 4/E RANDALL D. KNIGHT

Introduction to Optical Modeling. Friedrich-Schiller-University Jena Institute of Applied Physics. Lecturer: Prof. U.D. Zeitner

Imaging Overview. For understanding work in computational photography and computational illumination

What will be on the midterm?

ME 6406 MACHINE VISION. Georgia Institute of Technology

Computational Approaches to Cameras

SNC2D PHYSICS 5/25/2013. LIGHT & GEOMETRIC OPTICS L Converging & Diverging Lenses (P ) Curved Lenses. Curved Lenses

Optical Components for Laser Applications. Günter Toesko - Laserseminar BLZ im Dezember

VC 16/17 TP2 Image Formation

The Camera : Computational Photography Alexei Efros, CMU, Fall 2008

Photolithography II ( Part 2 )

INDEX OF REFRACTION index of refraction n = c/v material index of refraction n

Transcription:

Image Formation and Camera Design Spring 2003 CMSC 426 Jan Neumann 2/20/03

Light is all around us! From London & Upton, Photography

Conventional camera design... Ken Kay, 1969 in Light & Film, TimeLife Publ.

... is copying from human eyes! From I.Rock, Perception

Model: Pinhole Camera! Infinitely small aperture! infinite field of depth! Image is dim! Projection Equation:

Sensor Response! The response of a sensor is proportional to the radiance visible to the sensor.

Measurement Equation! Scene Radiance L(x,ω,t,λ)! Optics (x 0,ω 0 ) = T(x,ω,λ)! Pixel Response P(x,λ)! Shutter S(x,ω,t)

Collection! For a camera to be efficient, the pinhole is replaced by a lens.! The lens redirects light rays emanating from the object.

Refraction! Light slows down in materials.! Imagine a line of marching Girl Scouts... Direction of travel

Girl Scouts in the Mud Mud! As the marching line steps into the mud, they will slow down, depending on how thick the mud is.

Wavefronts at Normal Angle of Incidence

Girl Scouts in Mud at an Angle! The direction of travel changes when the marching line hits the mud at a non-normal angle.

Wavefront at Non-Normal Angle of Incidence

Index of Refraction! Index of Refraction (n) is the ratio between the speed of light in vacuum (c) and the speed of light in the medium (v). n = c/v Medium Index of Refraction Vacuum 1 (exactly) Air 1.0003 Water 1.33 Glass 1.5 Diamond 2.4

Snell s Law normal AIR GLASS GLASS AIR! This change in direction is described by Snell s Law

Trigonometry Review RULES THAT DEFINE SIN, COS, TAN of an ANGLE: Adjacent Side (x) θ Hypotenuse (r)! sin(θ) = y/r (opp/hyp)! cos(θ) = x/r (adj/hyp)! tan(θ) = y/x (opp/adj) Opposite Side (y)

Snell s Law θ 1 n 1 n 2 θ 2 Snell s Law: n 1 sinθ 1 = n 2 sinθ 2 If θ 1 and θ 2 are small use 1 st order approximation n 1 θ 1 = n 2 θ 2 (known as paraxial raytracing)

Refraction for Different Materials light 45 AIR WATER GLASS 16 28 32 DIAMOND

Convex Lens Object side Image side Light Rays F F Axis of symmetry Lens! Image focal point, F, is half the distance to the effective center of curvature of the lens.! Object focal point, F, is exactly the same distance on the object side of the lens.

Convex Lens f f F F! Image focal length, f, is the distance from the lens to the image focal point.! Object focal length, f, is the distance from the lens to the object focal point.

Camera Parameters! Field of view (film size, stops and pupils)! Depth of field (aperture, focal length)! Motion blur (shutter)! Exposure (film speed, aperture, shutter)

Depth of Field! From London and Upton

Circle of Confusion

Thin Lens Camera! Use lens to capture more light! Limited Depth of Field! Thin Lens Eq.:! Circle of Confusion:

Thin Lens Camera! Thin Lens Eq.:! Projection Eq.:

Model: Pinhole Camera! Infinitely small aperture! infinite field of depth! Image is dim! Projection Equation:

Thick Lens! Cutaway section of a Vivitar Series 1 90 mm f 1/2.5 lens Cover photo, Kingslake, Optics in Photography

Thick Lens Focal lengths are measured with respect to principal planes

Thick Lens Camera! A real lens has thickness

Field of View! From London and Upton

Field of View! From London and Upton

Illuminance Small aperture Large aperture! Illuminance is the rate of light falling on a given area (i.e. energy per unit time).! Illuminance is controlled by aperture: a larger aperture brings more light to the focus.

Aperture! Stops physical limits! Pupils logical limits for entering and exiting rays

Aperture

Exposure Lens camera Aperture Shutter! Exposure is defined as the total amount of light falling on the film.! Exposure = Illuminance * Time

Exposure Time Shutter Closed Shutter Open! Exposure time is controlled by the shutter: when closed, the film is not exposed to light.! Exposure time is simply the time interval between opening and closing the shutter.

Types of Shutters Simplified Camera Between the Lens (BTL) Or Leaf Shutter Focal Plane Shutter

BTL or Leaf Shutter CLOSED! Made of overlapping leaves that slide out of the way when shutter opens.! Located between the imaging lens elements. OPEN

BTL or Leaf Shutter! Advantages! Uniform illumination independent of film size.! Entire film frame illuminated at once.! Disadvantages! Illumination of frame not constant over time.! Limitations on shutter speed.

Focal Plane Shutter! Metal or fabric with a narrow slit opening which traverses the area to be exposed.! Located just before the detector (film) at the focal plane.

Focal Plane Shutter! Advantages! Cost effective (one shutter needed for all lenses - great for interchangeable lens systems)! Can achieve very fast shutter speeds (~1/10000 sec)! Disadvantages! May cause time distortion if the film size is large (since the shutter slit must traverse the film)

Why control exposure with aperture and shutter?! Flexibility!! Fast shutter speed for freezing action (e.g. sports photography).! Slow shutter speed for low light levels (e.g. sunsets).! Small aperture for bright scenes or to enable longer exposures.! Large aperture for low light conditions (taking candle lit or moon lit pictures).

Aperture vs Shutter! From London and Upton

Image Irradiance and Exposure! 1 stop doubles exposure! interacts with depth of field! Double shutter time doubles exposure! interacts with motion blur

High Dynamic Range Imaging! 16 photographs of stanfords memorial cathedral at 1 stop increments from 30s to 1/1000s! From Debevec and Malik, High Dynamic Range Imaging

Simulated HDR Image

Dispersion! Dispersion - Index of refraction, n, depends on the frequency (wavelength) of light. Dispersion is responsible for the colors produced by a prism: red light bends less within the prism, while blue light bends more.

Chromatic Aberration White light Object (small dot) F Blue F Red Image with chromatic aberration! Dispersion results in a lens having different focal points for different wavelengths - this effect is called chromatic aberration.! Results in a halo of colors.! Solution: Use 2 lenses of different shape and material ( achromatic doublet )..

Spherical Aberration F! All the rays do not bend toward the focal point, resulting in a blurred spot.! Solution: use lenses with aspherical curvature, or use a compound lens. Object (small dot) Image with spherical aberration.

Other Aberrations! Coma! Off axis blur which looks like the coma of a comet.! Astigmatism! Different focal lengths for different planes...! Distortion! Images formed out of shape.

So Far... AgX film camera processing image! AgX photographic film captures image formed by the optical elements (lens).! Unfortunately, the processing for film is slow (among other disadvantages).! Can we use something else to capture the image?

Charge Coupled Device (CCD)! CCD replaces AgX film! Based on silicon chip! Disadvantages vs. AgX: Light Sensitive Area! Difficulty/cost of CCD manufacture; large arrays are VERY expensive! Young technology; rapidly changing

Basic structure of CCD Divided into small elements called pixels (picture elements). Shift Register Rows Image Capture Area Columns preamplifier Voltage out

Magnified View of a CCD Array Individual pixel element CCD Close-up of a CCD Imaging Array

Spatial Sampling Scene Grid over scene Spatially sampled scene! When a continuous scene is imaged on the array (grid) formed by a CCD, the continuous image is divided into discrete elements.! The picture elements (pixels) thus captured represent a spatially sampled version of the image.

Quantization Spatially sampled scene 0 0 0 0 0 0 0 0 0 0 0 25 40 40 40 25 0 0 0 0 0 0 0 0 25 40 40 40 25 40 64 64 64 40 25 64 97 97 97 64 40 64 97 150 97 64 40 64 97 97 97 64 40 40 25 64 64 64 25 40 40 40 25 40 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Numerical representation 0 0! Spatially sampled image can now be turned into numbers according to the brightness of each pixel.

Response of CCD! The response of CCD is linear (i.e., if 1000 captured photons corresponds to a digital count of 4, then 2000 photons captured yields a digital count of 8)! Linearity is critical for scientific uses of CCD Response of photographic negative Response of CCD Density Digital Count Log H Exposure

Image quality factors! Two major factors which determine image quality are:! Spatial resolution -- controlled by spatial sampling.! Color depth -- controlled by number of colors or grey levels allocated for each pixel! Increasing either of these factors results in a larger image file size, which requires more storage space and more processing/display time.

CCDs as Semiconductors Insulator Conductor! Conductors allow electricity to pass through. (Metals like copper and gold are conductors.)! Insulators do not allow electricity to pass through. (Plastic, wood, and paper are insulators.)! Some materials are halfway in between, called semiconductors.

Basic structure of a pixel in a CCD Metal gate Silicon base Oxide Layer One pixel! Silicon is a semiconductor.! Oxide layer is an insulator.! Metal gates are conductors.! Made with microlithographic process.! One pixel may be made up of two or more metal gates.

Photon/Silicon Interaction e - Silicon! Photon knocks off one of the electrons from the silicon matrix.! Electron wanders around randomly through the matrix.! Electron gets absorbed into the silicon matrix after some period.

Spectral Response (sensitivity) of a typical CCD UV Visible Light IR Relative Response 300 400 500 600 700 800 900 1000 Incident Wavelength [nm]! Response is large in visible region, falls off for ultraviolet (UV) and infrared (IR)

Goal of CCD Photons CCD Electronic Signal! Capture electrons formed by interaction of photons with the silicon! Measure the electrons from each picture element as a voltage

Collection stage Voltage! Voltage applied to the metal gates produces a depletion region in the silicon. (depleted of electrons)! Depletion region is the light sensitive area where electrons formed from the photon interacting with the silicon base are collected.

Collection stage Voltage e - e -! Electron formed in the silicon matrix by a photon.! Electron wanders around the matrix.! If the electron wanders into the depletion region, the electron is captured, never recombining with the silicon matrix.

Collection Light e -e- e -e- e e- ē - e - e - e - e -! The number of electrons accumulated is proportional to the amount of light that hit the pixel.! There is a maximum number of electron that these wells can hold.

Readout! Now that the electrons are collected in the individual pixels, how do we get the information out? Alright! How do we get the electrons out?!

Readout! How do you access so much data efficiently? (i.e. a 1024 x 1024 CCD has 1,048,576 pixels!)! Possible solutions:! 1. Have output for individual pixels.! Too many wires! 2. Somehow move the charges across the CCD array and read out one by one.! Bucket Brigade

Bucket Brigade! By alternating the voltage applied to the metal gates, collected electrons may be moved across the columns. e - e e- -e- e -e- e e- ē - - - e -e- e e- ē e- - e - e - e- e- e - e - e - e e - - e e - e - e -eē- - ē - e - e - e - e -

Bucket Brigade! Charge is marched across the columns into the shift register, then read out 1 pixel at a time. 200 transfers 100 transfers Shift Register 100 pixels 100 transfers 100 pixels 1 transfer

Converting Analog Voltages to Digital! Analog voltage is converted to a digital count using an Analog-to-Digital Converter (ADC)! Also called a digitizer! The input voltage is quantized:! Assigned to one of a set of discrete steps! Steps are labeled by integers! Number of steps determined by the number of available bits! Decimal Integer is converted to a binary number for computation 6.18 volts 01100101 (117) ADC

Biological camera design is purposive camera design Landscape of Eye Evolution from R. Dawkins Climbing Mount Improbable

A biological camera

A biological camera

Omni-directional vision

Light field cameras From Levoy & Hanrahan Light Field Rendering, SIGGRAPH96 Wilburn et al., 02

Integral photography! Integral photography (Lippman 08, Ives 30, Naemura `01)

Plenoptic cameras Adelson & Wang 92 Farid & Simoncelli 97

Does a computer need to see the world with our eyes? Example: Epipolar Image Volume

Does a computer need to see the world with our eyes? Picture made using the VideoCube program by M. Cohen