Digital Photographs and Matrices

Similar documents
Digital Photographs, Image Sensors and Matrices

Digital Photographs and Matrices

Where Vision and Silicon Meet

History and Future of Electronic Color Photography: Where Vision and Silicon Meet

Topic 9 - Sensors Within

Megapixels and more. The basics of image processing in digital cameras. Construction of a digital camera

Digital Imaging Rochester Institute of Technology

University Of Lübeck ISNM Presented by: Omar A. Hanoun

Digital Cameras The Imaging Capture Path

CS559: Computer Graphics. Lecture 2: Image Formation in Eyes and Cameras Li Zhang Spring 2008

brief history of photography foveon X3 imager technology description

Improved sensitivity high-definition interline CCD using the KODAK TRUESENSE Color Filter Pattern

Cameras CS / ECE 181B

Digital Camera Sensors

General Imaging System

History and Future of Electronic Color Photography: Where Vision and Silicon Meet

Digital Imaging with the Nikon D1X and D100 cameras. A tutorial with Simon Stafford

Image acquisition. In both cases, the digital sensing element is one of the following: Line array Area array. Single sensor

Digital camera. Sensor. Memory card. Circuit board

Cameras. Outline. Pinhole camera. Camera trial #1. Pinhole camera Film camera Digital camera Video camera

Lecture 29: Image Sensors. Computer Graphics and Imaging UC Berkeley CS184/284A

Introduction to Computer Vision

Digital Cameras. Consumer and Prosumer

Solid state image sensors and pixels

Chapters 1-3. Chapter 1: Introduction and applications of photogrammetry Chapter 2: Electro-magnetic radiation. Chapter 3: Basic optics

Unit 1: Image Formation

Cameras. Outline. Pinhole camera. Camera trial #1. Pinhole camera Film camera Digital camera Video camera High dynamic range imaging

Cameras. Digital Visual Effects, Spring 2008 Yung-Yu Chuang 2008/2/26. with slides by Fredo Durand, Brian Curless, Steve Seitz and Alexei Efros

RGB RESOLUTION CONSIDERATIONS IN A NEW CMOS SENSOR FOR CINE MOTION IMAGING

Digital photography , , Computational Photography Fall 2017, Lecture 2

Photons and solid state detection

VC 11/12 T2 Image Formation

Cameras. Shrinking the aperture. Camera trial #1. Pinhole camera. Digital Visual Effects Yung-Yu Chuang. Put a piece of film in front of an object.

CS 89.15/189.5, Fall 2015 ASPECTS OF DIGITAL PHOTOGRAPHY COMPUTATIONAL. Sensors & Demosaicing. Wojciech Jarosz

F-number sequence. a change of f-number to the next in the sequence corresponds to a factor of 2 change in light intensity,

Chapter 2-Digital Components

Victoria RASCals Star Party 2003 David Lee

Dr F. Cuzzolin 1. September 29, 2015

Advanced Camera and Image Sensor Technology. Steve Kinney Imaging Professional Camera Link Chairman

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

DIGITAL CAMERA SENSORS

Putting It All Together: Computer Architecture and the Digital Camera

VC 14/15 TP2 Image Formation

Detectors for microscopy - CCDs, APDs and PMTs. Antonia Göhler. Nov 2014

Machine Vision: Image Formation

Lecture 2: Digital Image Fundamentals -- Sampling & Quantization

How does prism technology help to achieve superior color image quality?

Professional digital SLRs keep getting better faster DIG IT

Vision, Color, and Illusions. Vision: How we see

Chapters 1-3. Chapter 1: Introduction and applications of photogrammetry Chapter 2: Electro-magnetic radiation. Chapter 3: Basic optics

Digital Cameras vs Film: the Collapse of Film Photography Can Your Digital Camera reach Film Photography Performance? Film photography started in

VC 16/17 TP2 Image Formation

Image Formation and Capture

Announcement A total of 5 (five) late days are allowed for projects. Office hours

VLSI DESIGN OF A HIGH-SPEED CMOS IMAGE SENSOR WITH IN-SITU 2D PROGRAMMABLE PROCESSING

Digital photography , , Computational Photography Fall 2018, Lecture 2

CS6670: Computer Vision

The Raw Deal Raw VS. JPG

Digital Photography. Visual Imaging in the Electronic Age Lecture #8 Donald P. Greenberg September 14, 2017

Chapter 4: Image Transfer Choosing a Computer

TRUESENSE SPARSE COLOR FILTER PATTERN OVERVIEW SEPTEMBER 30, 2013 APPLICATION NOTE REVISION 1.0

Charged Coupled Device (CCD) S.Vidhya

Digital Photographic Imaging Using MOEMS

NEW 35MM CMOS IMAGE SENSOR FOR DIGITAL CINE MOTION IMAGING

Lecture 7. July 24, Detecting light (converting light to electrical signal)

CPSC 4040/6040 Computer Graphics Images. Joshua Levine

light sensing & sensors Mo: Tu:04 light sensing & sensors 167+1

Building a Real Camera

Astronomical Cameras

Contents. Image Quality Megapixel CCD sensors. Higher resolution produces greater detail

Cameras. CSE 455, Winter 2010 January 25, 2010

Image Formation and Camera Design

Images and Displays. Lecture Steve Marschner 1

Development Challenges of a New Image Capture Technology: Foveon X3 Image Sensors

COLOR FILTER PATTERNS

MEM455/800 Robotics II/Advance Robotics Winter 2009

Assignment: Cameras and Light

Image Processing COS 426

Charge-Coupled Device (CCD) Detectors pixel silicon chip electronics cryogenics

LENSES. INEL 6088 Computer Vision

EE 392B: Course Introduction

Digital Cameras vs Film: the Collapse of Film Photography Can Your Digital Camera reach Film Photography Performance? Film photography started in

Capturing Light in man and machine. Some figures from Steve Seitz, Steve Palmer, Paul Debevec, and Gonzalez et al.

VGA CMOS Image Sensor

Cvision 2. António J. R. Neves João Paulo Silva Cunha. Bernardo Cunha. IEETA / Universidade de Aveiro

Color image Demosaicing. CS 663, Ajit Rajwade

Operation and performance of a color image sensor with layered photodiodes

CHARGE-COUPLED DEVICE (CCD)

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

Application of CMOS sensors in radiation detection

Simultaneous geometry and color texture acquisition using a single-chip color camera

Using raw files from digital cameras

Image Formation: Camera Model

DIGITAL IMAGING. Handbook of. Wiley VOL 1: IMAGE CAPTURE AND STORAGE. Editor-in- Chief

PROCESSING X-TRANS IMAGES IN IRIDIENT DEVELOPER SAMPLE

Part Number SuperPix TM image sensor is one of SuperPix TM 2 Mega Digital image sensor series products. These series sensors have the same maximum ima

Building a Real Camera. Slides Credit: Svetlana Lazebnik

Lecture Notes 11 Introduction to Color Imaging

PIXPOLAR WHITE PAPER 29 th of September 2013

Ultra-high resolution 14,400 pixel trilinear color image sensor

Transcription:

Digital Photographs and Matrices Digital Camera Image Sensors Electron Counts Checkerboard Analogy Bryce Bayer s Color Filter Array Mosaic. Image Sensor Data to Matrix Data Visualization of Matrix Addition and Scalar Multiplication Different Technologies Charge Coupled Devices Direct Image Sensor Foveon Image Sensor in the Sigma SD14 Camera

Digital Camera Image Sensors The physical appearance of an image sensor is an electronics package which can be imagined to replace the film in a standard film camera. Figure 1. An IBM Image sensor

Electron Counts Images focused through the camera lens cause photons to hit image sensor photosites and knock loose electrons, which are then captured and stored. The electron count at a photosite is directly proportional to the light intensity. When the image is read off the sensor, the stored electrons are converted to a series of analog charges which are then converted to integer counts by an analog to digital converter (ADC). 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 2 2 3 4 3 1 1 1 1 5 6 5 5 4 4 1 1 1 1 5 4 3 3 4 5 1 1 1 1 5 4 5 5 5 5 1 1 1 1 5 5 5 6 6 5 1 1 1 1 3 3 2 2 3 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Figure 2. Integer counts of light intensity on an image sensor. One unit of intensity corresponds to a certain large electron count at the photosite.

The Checkerboard Analogy After the image is stored, the light intensity data is like a checkerboard with so many checkers stacked on each square. Figure 3. Checkerboard Analogy of Image Sensor Data Monochrome Intensities to Color Photo The monochrome intensity data is post-processed to create the corresponding color image that is stored on disk and viewed on the camera s LCD screen. Details next.

The Bayer Mosaic Bryce Bayer at Kodak invented a mosaic, which is a pattern of red, green and blue filters arranged on a grid resembling a checkerboard. Half the squares are green, because the human eye is not equally sensitive to all three colors. The image sensor is an array of photodiodes with the Bayer color filter array atop, and a matching array of light-focusing micro-lenses atop each filter [3 layers like a BLT sandwich]. Figure 4. Bryce Bayer s Mosaic Color Filter Array. A digital camera stores raw image sensor data as a matrix A of numbers. The numbers correspond to the light intensity at a red, green or blue filter site. The photosensor sits below the filter and estimates intensity as the number of electrons knocked off by incident photons.

Image Sensor Data to Matrix Data The matrix A of photosensor intensities representing the Bayer mosaic must be translated to a matrix B of RGB data and then stored in TIFF or JPG format for print processing. In an image sensor for 24-bit color, a photo pixel is RGB interpolated data from 4 or more adjacent Bayer mosaic filter sites. The process of using other filter squares in the neighborhood of a sensor site to make an educated guess of the RGB data is called interpolation or de-mosaicing. A matrix B which stores the interpolated RGB data has one entry for each mosaic filter square. The square s new intensity data is a coded integer a. One scheme is a = r + (256)g + (65536)b. Symbols r, g, b are integers between 0 and 255 which specify the interpolated intensity of colors red, green and blue, respectively. Computer experts recognize this encoding as bit shifting.

Visualization of Matrix Addition and Scalar Multiplication Matrix addition can be visualized through matrices representing color separations [idea due to James Clerk Maxwell; browse Wikipedia]. When three monochrome transparencies of colors red, green and blue (RGB) are projected simultaneously by a projector, the colors add to make a full color screen projection. The three transparencies can be associated with matrices R, G, B which contain pixel data for the monochrome images. Then the projected image is associated with the matrix sum R + G + B. Scalar multiplication of matrices has a similar visualization. The pixel information in a red, green or blue monochrome image is coded for intensity. The associated matrix A of pixel data when multiplied by a scalar k gives a new matrix ka of pixel data with the intensity of each pixel adjusted by factor k. The photographic effect is to adjust the range of intensities. In the checkerboard visualization of an image sensor, factor k increases or decreases the tri-color checker stack height at each square.

Different Technologies The Foveon X3 sensor has three layers of photodiodes embedded at different silicone strata levels, emulating the three emulsion layers of color film [fovea is an area on the retina]. The sensor requires no interpolation. An X3 sensor with 4.7MP for each of red, green, blue colors was used in the Sigma SD14 digital camera, boasting 14MP and a host of advantages over Bayer arrays. Kodak sensor technology from 2007 uses the Bayer pattern but adds panchromatic squares to boost luminance data. This sensor realizes one to two F-stops of low light performance. Reference A short course on sensors and digital cameras can be found at http://www.shortcourses.com/sensors/index.html

Charge Coupled Devices Digital cameras replace film with a charge-coupled device, or CCD. The CCD is a semiconductor with a checkerboard of capacitors that each store an electrical charge. Each photosite of the CCD can be imagined to produce one pixel of a digital image. When the camera shutter is opened, photons of light hit the CCD and knock loose electrons. The number of electrons displaced is proportional to the intensity of light, thus forming an image. The image is read from the CCD device by a method called coupling, judged too technical for discussion here. Direct Image Sensor The direct image sensor is a CMOS [complementary metal-oxide semiconductor] device designed to replicate the separate red-green-blue light recording of a charge-coupled device in the film model of three emulsion layers. To do this, light intensity recording layers are embedded in silicon, because red, green, and blue light penetrate silicon at different depths. This allows the direct image sensor to record original light intensity at each depth, because light not absorbed at one level continues to the next level.

Figure 5. Foveon X3 direct image sensor.

Foveon Sensor in the Sigma SD14 Digital Camera 2007 The Foveon X3 Model F19X3-A50 is a low-cost 4.7MP CMOS sensor that boasts 14MP resolution without interpolation. The manufacturer s claims: Foveon X3 Technology Three pixel sensors are layered in silicon to achieve full-measured color. Images have improved sharpness and immunity to sampling artifacts (moiré). Ultra Low Power Ultra low power requirements. Power consumption is less than 200mW during readout, less than 40mW in standby mode,and less than 1mW in power down mode. Low Noise Extremely low-noise readout and high dynamic range. Suppression of fixed pattern noise artifacts associated with CMOS image sensors.