Resolution Loss without Optical Blur Tali Treibitz Alex Golts Yoav Y. Schechner Technion, Israel 1.

Slides:



Advertisements
Similar presentations
“Instant 3Descatter” Tali Treibitz and Yoav Y. Schechner CVPR
Advertisements

Road-Sign Detection and Recognition Based on Support Vector Machines Saturnino, Sergio et al. Yunjia Man ECG 782 Dr. Brendan.
Some problems... Lens distortion  Uncalibrated structure and motion recovery assumes pinhole cameras  Real cameras have real lenses  How can we.
Strata: Layered Coding for Scalable Visual Communication Wenjun Hu Jingshu Mao Zihui Huang Yiqing Xue Junfeng She Kaigui Bian Guobin (Jacky) Shen.
May 4, 2015Kyle R. Bryant Tutorial Presentation: OPTI521 Distance 1 MTF Definition MTF is a measure of intensity contrast transfer per unit resolution.
Resident Physics Lectures
Generalized Mosaics Yoav Y. Schechner, Shree Nayar Department of Computer Science Columbia University.
Digital Image Processing In The Name Of God Digital Image Processing Lecture3: Image enhancement M. Ghelich Oghli By: M. Ghelich Oghli
Medical Imaging Mohammad Dawood Department of Computer Science University of Münster Germany.
BMME 560 & BME 590I Medical Imaging: X-ray, CT, and Nuclear Methods
Edge detection. Edge Detection in Images Finding the contour of objects in a scene.
MSU CSE 803 Stockman Linear Operations Using Masks Masks are patterns used to define the weights used in averaging the neighbors of a pixel to compute.
Dept. Elect. Eng. Technion – Israel Institute of Technology Ultrasound Image Denoising by Spatially Varying Frequency Compounding Yael Erez, Yoav Y. Schechner,
Fiber-Optic Communications
MSU CSE 803 Linear Operations Using Masks Masks are patterns used to define the weights used in averaging the neighbors of a pixel to compute some result.
Removing Weather Effects from Monochrome Images Srinivasa Narasimhan and Shree Nayar Computer Science Department Columbia University IEEE CVPR Conference.
8.1 PRODUCTION AND CHARACTERISTICS OF X-RAYS
Despeckle Filtering in Medical Ultrasound Imaging
Basic Principles of Light Polarization Lecture #17 Thanks to Yoav Schechner, Nayar, Larry Wolff.
Seeram Chapter 11: Image Quality
Mohammed Rizwan Adil, Chidambaram Alagappan., and Swathi Dumpala Basaveswara.
RF background, analysis of MTA data & implications for MICE Rikard Sandström, Geneva University MICE Collaboration Meeting – Analysis session, October.
Different sources of noise in EM-CCD cameras
CSCE 441: Computer Graphics Image Filtering Jinxiang Chai.
Tone mapping with slides by Fredo Durand, and Alexei Efros Digital Image Synthesis Yung-Yu Chuang 11/08/2005.
Detecting Electrons: CCD vs Film Practical CryoEM Course July 26, 2005 Christopher Booth.
Analysis of Phase Noise in a fiber-optic link
Image Formation. Input - Digital Images Intensity Images – encoding of light intensity Range Images – encoding of shape and distance They are both a 2-D.
© by Yu Hen Hu 1 ECE533 Digital Image Processing Image Restoration.
Computer Vision – Enhancement(Part II) Hanyang University Jong-Il Park.
Active Vision Key points: Acting to obtain information Eye movements Depth from motion parallax Extracting motion information from a spatio-temporal pattern.
Overview of Haze Removal Methods Matteo Pedone Machine Vision Group, University of Oulu, Finland.
Flat Refractive Geometry Tali Treibitz, Yoav Y. Schechner Technion, Israel Hanumant Singh Woods Hole Oceanographic Institute.
Discrete Images (Chapter 7) Fourier Transform on discrete and bounded domains. Given an image: 1.Zero boundary condition 2.Periodic boundary condition.
BMME 560 & BME 590I Medical Imaging: X-ray, CT, and Nuclear Methods Introductory Topics Part 2.
Colour changes in a natural scene due to the interaction between the light and the atmosphere Raúl Luzón González Colour Imaging Laboratory.
Digital Imaging. Digital image - definition Image = “a two-dimensional function, f(x,y), where x and y are spatial coordinates, and the amplitude of f.
Lecture 3 The Digital Image – Part I - Single Channel Data 12 September
Medical Image Analysis Dr. Mohammad Dawood Department of Computer Science University of Münster Germany.
Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-image Raw-data Alessandro Foi, Mejdi Trimeche, Vladimir Katkovnik, and Karen Egiazarian.
Machine Vision ENT 273 Image Filters Hema C.R. Lecture 5.
Course 2 Image Filtering. Image filtering is often required prior any other vision processes to remove image noise, overcome image corruption and change.
Intelligent Vision Systems ENT 496 Image Filtering and Enhancement Hema C.R. Lecture 4.
EE 230: Optical Fiber Communication Lecture 12
Space Time Codes. 2 Attenuation in Wireless Channels Path loss: Signals attenuate due to distance Shadowing loss : absorption of radio waves by scattering.
1 Draft Motion Imagery Quality Equation (MIQE) Draft Motion Imagery Quality Equation (MIQE) March 2009 JACIE UNCLASSIFIED Dr. Darrell L. Young & Dr. Tariq.
Lecture 9 Feature Extraction and Motion Estimation Slides by: Michael Black Clark F. Olson Jean Ponce.
Polarization-based dehazing using two reference objects
X-ray SNR in 3 steps. I ∆I. X-ray transmission SNR Review Let N = average number of transmitted x-rays N = N 0 exp [ - ∫  dz ] Emission and transmission.
Machine Vision Edge Detection Techniques ENT 273 Lecture 6 Hema C.R.
Analysis of denoising filters for photo response non uniformity noise extraction in source camera identification Irene Amerini, Roberto Caldelli, Vito.
Non-linear filtering Example: Median filter Replaces pixel value by median value over neighborhood Generates no new gray levels.
Electro-optical systems Sensor Resolution
X-Ray Learning Objectives State general assumptions class makes Understand energy profile of X-ray source Duality Ideal projection X-ray imaging equation.
Flat Refractive Geometry Tali Treibitz, Yoav Y. Schechner Technion, Israel Hanumant Singh Woods Hole Oceanographic Institute.
Date of download: 6/26/2016 Copyright © 2016 SPIE. All rights reserved. Simulations and comparison of SOFI reconstructions with different optical pixel.
Instant Dehazing of Images using Polarization
Multiplexed Illumination
A Gentle Introduction to Bilateral Filtering and its Applications
Spatially Varying Frequency Compounding of Ultrasound Images
How do we realize design? What should we consider? Technical Visual Interaction Search Context of use Information Interacting/ transacting.
Single Image Haze Removal Using Dark Channel Prior
Basic Principles of Light Polarization Lecture #17
Digital Visual Effects, Spring 2006 Yung-Yu Chuang 2006/3/8
Basic Principles of Light Polarization Lecture #17
Linear Operations Using Masks
Elementary Mechanics of Fluids Lab # 3 FLOW VISUALIZATION
Elementary Mechanics of Fluids Lab # 3 FLOW VISUALIZATION
An Edge-preserving Filtering Framework for Visibility Restoration
Presentation transcript:

Resolution Loss without Optical Blur Tali Treibitz Alex Golts Yoav Y. Schechner Technion, Israel 1

14 airlight A 0 1 z scattering direct transmission D object radiance L object total intensity I 0 z Schechner, Narasimhan, Nayar

Haze airlightobjecttransmittance Schechner et al., Applied Optics ‘03 15

Pointwise Degradations object pointwise attenuation: vignetting atmosphere attenuation additive component: reflection glare path radiance noise Treibitz & Schechner, Recovery Limits in Pointwise Degradations 17

Pointwise Degradations object pointwise attenuation noise Treibitz & Schechner, Recovery Limits in Pointwise Degradations reduce SNR even if known additive (positive) bias 18

Noise: Object size matters? 19

Noise: Object size matters? 20

Noise: Object size matters? 21

Noise: Object size matters? 22

depends on: noise level object  background intensity difference object size quantify this dependency ! Prior art: resolution limits due to optical blur here: no optical blur Visibility Under Noise 23

Previous criteria Is there something there? Is it a tank? What type is it? Johnson charts: identificationrecognitionorientationdetection tank minimum line pairs for 50% success

NIIRS- National Image Interpretability Rating Scales Identify the wing configuration (e.g., straight, swept, delta) of all large aircraft (e.g., 707, CONCORD, BEAR, BLACKJACK)... Detect large hangars at airfields. Detect large static radars (e.g., AN/FPS-85, COBRA DANE, PECHORA, HENHOUSE), Detect military training areas... Detect a medium-sized port facility and/or distinguish between taxi-ways and runways at a large airfield. Interpretability of the imagery is precluded by obscuration, degradation, or very poor resolution Identify small light-toned ceramic insulators that connect wires of an antenna. Identify vehicle registration numbers (VRN) on trucks. Identify screws and bolts on missile components

pattern visible Treibitz & Schechner, Recovery Limits in Pointwise Degradations 24

Where is the Cutoff? pattern visible pattern invisible calculated analytically! Treibitz & Schechner, Recovery Limits in Pointwise Degradations Input SNR 25 (frequency)

Treibitz & Schechner, Recovery Limits in Pointwise Degradations 26 Cutoff Per Success Rate success rate 50% Input SNR

Noise Suppression in the HVS Theoretical Neuroscience, Dayan & Abbott frequency (cycles/degree) response of receptive field low noise high noise We derive: fundamental analytical model Model: simple linear denoising not a denoising method 28

SNR Improvement by Averaging signal noise Treibitz & Schechner, Recovery Limits in Pointwise Degradations - SNR change after averaging 29

Different Sizes of Windows too big for signaltoo small for noise Treibitz & Schechner, Recovery Limits in Pointwise Degradations 30

Averaging by Optimal Window Treibitz & Schechner, Recovery Limits in Pointwise Degradations 31

SNR Improvement by Averaging depends on frequency! Treibitz & Schechner, Recovery Limits in Pointwise Degradations same plot for a Gaussian filter 32

Output SNR Treibitz & Schechner, Recovery Limits in Pointwise Degradations Input SNR (frequency) 33

Cutoff Per Success Rate success rate 70% success rate 40% Treibitz & Schechner, Recovery Limits in Pointwise Degradations Input SNR (frequency) 34

Vision Success is Probabilistic SNR=2/3 SNR determines chances of visibility visible invisible Treibitz & Schechner, Recovery Limits in Pointwise Degradations 35

Success within a Confidence Interval - success rate SNR Treibitz & Schechner, Recovery Limits in Pointwise Degradations Object is visible if …depends on SNR and.. 36

25 Success within a Confidence Interval - success rate SNR Treibitz & Schechner, Recovery Limits in Pointwise Degradations what is the probability for correct detection? depends on SNR object pixel background pixel visibility is kept if edge keeps sign %(sign kept) - %(wrong sign) noisyclear

system input SNR Determining Resolution Limits cutoff for ρ=70% success frequency Treibitz & Schechner, Recovery Limits in Pointwise Degradations 37

Pointwise Degradations pointwise attenuation: vignetting atmosphere attenuation additive component: reflection glare haze noise Treibitz & Schechner, Recovery Limits in Pointwise Degradations 38

Noise Model Nikon D100 photon noise dominates 39 Treibitz & Schechner, Recovery Limits in Pointwise Degradations

Detector (pixel) 50% quantum efficiency Schechner Photon (shot) Noise 9 Electrons Photon or e { nothing either

50% quantum efficiency Schechner 10 Photons Electrons e { nothing e e either Photon (shot) Noise

SNR per size (frequency) Treibitz & Schechner, Recovery Limits in Pointwise Degradations 41

Resolution Limits in Haze distance [km] limit due to pixel size limit due to atmosphere Treibitz & Schechner, Recovery Limits in Pointwise Degradations minimal visible object size[m] reciprocal to 42

(frequency) Treibitz & Schechner, Recovery Limits in Pointwise Degradations 43 Cutoff Per Success Rate success rate 50% Input SNR

Haze in the Galilee Treibitz & Schechner, Recovery Limits in Pointwise Degradations average of 50 frames raw frame limit due to noise and not blur 44

What now? What are the reconstruction limits? What is the minimal detectable object size? What camera noise properties are acceptable for detection? … 45

Imaging in Haze Treibitz & Schechner, Polarization- Beneficial for Visibility Enhancement? 46

Treibitz & Schechner, Polarization- Beneficial for Visibility Enhancement? Haze Through a Polarizer best polarized image 47

Treibitz & Schechner, Polarization- Beneficial for Visibility Enhancement? single frame- used by photographers Haze Through a Polarizer increased exposure time best polarized image 48

Treibitz & Schechner, Polarization- Beneficial for Visibility Enhancement? two frames- Schechner et al. Dehazing using a Polarizer post-processing 2 frames best polarized image worst polarized image 49

 goal: object detection  local contrast stretch- OK Treibitz & Schechner, Polarization- Beneficial for Visibility Enhancement? Is it worth using a polarizer? unpolarized image best polarized image post-processing 2 frames rarely! under the constraint of equal acquisition time 50

degree of polarization Using a Single Polarized Image Best polarized image I min 51 Treibitz & Schechner, Polarization- Beneficial for Visibility Enhancement?

SNR Comparison 52 Treibitz & Schechner, Polarization- Beneficial for Visibility Enhancement?

A Single Saturated Frame Treibitz & Schechner, Polarization- Beneficial for Visibility Enhancement? SNR polarized SNR unpolarized maximal value in nature 53

SNR Comparison equal acquisition time technical details in the paper acquisition time = exposure time X number of frames 54

Treibitz & Schechner, Polarization- Beneficial for Visibility Enhancement? Same Total Acquisition Time p<0.4 in our experiments SNR polarized SNR unpolarized maximal value in nature 55

Experiment Wide field of view average of 2 frames Treibitz & Schechner, Polarization- Beneficial for Visibility Enhancement? same total acquisition time 56

dehazing SNR Comparison technical details in the paper optimal exposures equal acquisition time 57

Advantages of Polarization distance map contrast stretch in non-uniform distances restoring color compensating for attenuation 58

Freq cutoff – due to noise – without imaging blur Relation between cutoff and success rate Application: limits in pointwise degradations Limits in Pointwise Degradations Case study of performance trade-offs 59