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Master ISTI / PARI / IV
Introduction to Astronomical Image Processing
2. Observation: instruments & sensors
André Jalobeanu
LSIIT / MIV / PASEO group
Dec. 2005
lsiit-miv.u-strasbg.fr/paseo
PASEO
Observation: instruments & sensors
Telescopes (physics, examples)
Problems using telescopes
Diffraction limit
Atmospheric turbulence and opacity
Complex hardware solutions
Interferometry (multiple instruments)
Active and Adaptive optics
2D sensors
Optical imaging (UV, visible, IR), Radio, X, Gamma
Multispectral imaging with 2D sensors
3D sensors: integral field spectroscopy (IFS)
Hyperspectral imaging: fibers, slicers, lensarrays
Summary: the image acquisition chain
Astronomical telescopes:
an overview
Grasp image formation concepts
(optical and non-optical devices)
Understand the problems related
to diffraction and atmosphere
Be aware of advanced hardware
solutions specific to astronomy
Optical telescopes: introduction
(UV, visible, IR)
20cm amateur
telescope
50cm refractor (Nice)
8.2m Very Large Telescope
(VLT) #1
2.5m Nordic
Optical Telescope
2.4m Hubble Space Telescope (HST)
Optical reflecting telescopes: principle
Optical tube assembly
Primary mirror:
Light collector
Parabolic shape (in general)
Secondary mirror
Reflection (get the light out)
Correction in some cases
Different designs, various applications
Newton
Cassegrain
Maksutov
Schmidt Camera
X-ray telescopes
Grazing angle reflection only!
Nested metallic mirrors
Paraboloid+Hyperboloid surfaces
Space telescopes
X-rays are deflected by radiation belts
Problems
Focusing on the entire field of view
High-energy particles in deep space
58 nested X-ray
grazing incidence mirrors
(XMM-Newton, ESA)
Radio Telescopes
Parabolic antennas
Metallic dishes, rough surface comp. to optical telescopes!
Aperture synthesis
Mix signals from a collection of instruments (or use Earth rotation)
Each instrument provides an image sample in the Fourier space
NRAO Very Large Array (VLA)
Problem:
Sparse sampling of the
Fourier space
Diffraction and finite-size instruments
Ideal instrument, circular aperture D
Airy pattern point spread function (PSF)
“hat” modulation transfer function (MTF)
larger aperture size,
sharper image...
and higher cost!
MTF
Airy pattern
1.22λ/D
PSF
MTF = autocorrelation of the pupil function
Pupil shape and MTF
©Damian Peach
resolution:
10 cm aperture
30 cm aperture
http://www.damianpeach.com/
Diffraction (monochromatic):
Optical aberrations and distortions
30 cm aperture
top: no spherical aberration
bottom: lambda/2 aberration
๏ Geometric distortions
‣ Barrel/cushion distortions
๏ Chromatic aberrations
‣ Dispersion through lenses (refractors)
©Damian Peach
๏ Monochromatic aberrations
‣ Spherical aberrations
‣ Astigmatism
‣ Coma
Atmospheric opacity
Atmospheric extinction and refraction
Extinction
Airmass depends on the zenith angle
Molecular absorption
Rayleigh scattering
Aerosol scattering
Wavelength-dependent effect!
Refraction
Varying atmospheric refraction index
and lens-shaped atmosphere
Zenith angle-dependent (airmass)
Wavelength-dependent (dispersion)
Sky background
๏ Atmosphere
‣ Sunlight scattering (dust, aerosols)
‣ Stray lights (light pollution)
‣ Sky emission lines
“Pinatubo” effect
‣
Europe by night
๏ Interplanetary medium
‣ Sunlight scattering (zodiacal light)
Zodiacal light
Atmospheric turbulence: short exposure
Short exposures
Distorted wave-front: phase error
Kolmogorov turbulence statistics
Speckles
(Max Planck Institute)
Binary star
Atmospheric turbulence - long exposure
Long exposures:
average over the (long) integration time!
Long exposure modulation transfer function:
−3.44·(λ f ν/r0)−5/3·[1−b·(λ f ν/D)1/3]
MTFs(ν) = e
ν
λ
f
D
br r0 spatial frequency
wavelength
focal length
aperture diameter
constant (1 for far-field)
Fried's seeing diameter
Equivalent diameter:
seeing diameter r0
Long exposure MTF
vs. telescope MTF
Aperture Synthesis Interferometry
hardware solution to the diffraction limit
Go beyond the diffraction limit?
Build larger telescopes...
or use multiple telescopes simultaneously!
Interference (2 telescopes):
Produce interference fringes (contrast+phase)
In practice: measure the amplitude, phase from “phase closure”...
(random phase fluctuations due to atmospheric turbulence)
Sample the Fourier (u,v) space
Interferometry - the VLTI
Angular resolution:
longest baseline
Problems:
Sparse sampling of
the Fourier space
Noise (amplitude)
Phase determination
Active and Adaptive Optics
hardware solutions to atmospheric turbulence
Wave-front sensor:
measure the phase error
Deformable mirror:
correct the wave-front
Sensors:
image sampling in 2D/3D
Understand how 2D sensors provide
single band or multispectral images
Have an idea of how 3D sensors
work (integral field spectroscopy)
Understand the problems related to
these sensors (e.g. noise)
2D sensors
Optical imaging (UV, visible, IR)
Optical detectors (from UV to IR):
CCD vs. CMOS: image transfer
image on the focal plane
Sensitive area (pixel)
CCD matrix
and pixel integration
Problems:
Quantum Efficiency (QE)
Poor in some wavelength ranges, wavelength-dep:
Noise (stochastic errors)
Photon counting, readout, cosmic...
Bias (systematic errors)
Dark current, non-linearity, bad pixels...
Blur caused by charge diffusion
Noise in electronic detectors
stochastic errors
Noise: stochastic process
Observation: realization of a random variable
Assumptions :
• Several additive processes, zero mean
• Stochastic independence between pixels (white noise)
• Stationary process (although parameters may be non-stationary)
 Probabilistic model: P(d | m) conditional pdf
value of the mean
observed pixel
• Counting: Poisson
• Readout: Gaussian
• Background (thermal, sky): ~ Poisson (shift m),
cosmic rays: impulse noise
• Quantization: uniform (bounded)
Simplification: high photon count ߜGaussian noise (variance = m ≈ d)
Bias in electronic detectors
systematic errors
๏ Dark current
‣ Background current in the detector, pixel-dependent
(both current and QE decrease with temperature)
๏ Non-linearity
‣ Non-linearity of the amplification process
‣ Pixel saturation, “blooming”
๏ Pixel-dependent sensitivity
‣ QE depends on each pixel: “flat-field” image
๏ Bad pixels
‣ Hot pixels: close to saturation
‣ Cold pixels: low efficiency or various electronic problems
๏ Cross-talk
‣ Charge diffusion between pixels (usually during transfer)
X-ray sensors
Photon imaging
Record time, position, energy
Use special CCD detectors (PN-CCD)
Over 90% QE, range 0.5-10keV
Problems:
Photon-counting noise
Particle noise (tracks)
Geminga
(neutron star)
X-ray CCD for XMM (MPE)
Gamma sensors
Use the Compton effect
2 layers: scintillator, photon detector
Record time, location, energy
Orientation from location on each layer
Problems:
Inaccurate orientation determination
Photon-counting noise
CGRO (NASA)
Radio sensors
Radio antenna technology (EM wave ߜ current)
Problems:
Sparse sampling of the Fourier space
Correlated noise on reconstructed images!
Galaxy 3C353, VLA 3.6cm (NRAO)
Multiband imaging with 2D sensors
Filter the light between telescope and sensor
Classical absorption filters (bandpass)
Fabry-Pérot interferometer (narrow band)
tunable filters: var. wavelength and bandwidth
Problems:
Non-simultaneous observations:
image registration needed!
Data cube scrambling: interference rings have to be removed
Time consuming for large number of bands (usually <30)
3D sensors: Integral Field Spectroscopy (IFS)
one spectrum for each pixel (“spaxel”)
Fiber-fed spectrograph
Optical fibers in the focal plane:
rearrange spatial information (field to slit mapping)
Feed a dispersion system and record spectra on a CCD sensor
Lenslet arrays: enhance sampling and sensitivity
GMOS Integral Field Unit (Durham University)
IFS - lenslet arrays
Array of small lenses
Solve problems related to sampling/sensitivity (gaps btw fibers)
No fiber transmission problem
Drawbacks: lower spectral resolution, scrambling on the sensor
OASIS optical layout (CFHT)
IFS - Image slicers, summary
Slicer mirror system-fed spectrograph
Cut focal plane into multiple slices
Solve problems related to fiber transmission
Problems: (IFS in general)
IFS Summary
Few spaxels (spatial elements): max 80x80 (300x300 in 2012...)
Unscrambling (CCD to field): correlated noise from resampling
Dimensionality & redundancy
GNIRS Integral Field Unit (Durham University)
Optical image acquisition chain schematics
deep-sky object
support
values
continuous
discrete
deterministic
atmosphere
optics
turbulence,
opacity
aberrations
telescope
diffraction
counting,
thermal,
cosmics
sensor
detector
motion,
defects
stochastic
physics
geometry, physics
scan
conversion
amp.
A/D
readout
noise
Observed
Image
A simplified optical observation model
deterministic values
stochastic values
noise
T
Natural scene
Blur
Sampling
continuous support
+
Trans.
observed image
discrete support
Convolution by PSF
(Point Spread Function)
multiply by
Dirac array
Pixel value
transform
additive noise
probabilistic model
Simplification :
Shift invariance
 mult. by MTF in freq. space
Simplification :
~ Nyquist
Simplification :
Identity
Simplification :
Gaussian, white,
stationary
OTF(ξ, η) = MTF(ξ, η) · PTF(ξ, η)
F[Y] = MTF.F[X] + N
variance σ2
Modeling the blur
origins and expressions
• Atmosphere : turbulence, diffusion
• Optics : diffraction, aberrations (defocus, spherical aberration, ...)
• Sensor (geometry) : integration, motion (drift, ...)
• Sensor (physics) : charge diffusion
MTF0 = MTFatm . MTFopt . MTFgeom . MTFphys
Global
MTF
Turbulence :
[Kolmogorov 41]
Diffusion:
Truncated Gaussian
Incoherent light
optics
- translation: sinc
- oscillations
Charge
diffusion:
Gaussian
Observation: instruments & sensors
Further reading (web links)
๏
Various Wikipedia articles
๏
Adaptive optics tutorial at CTIO (turbulence & AO)
๏
๏
๏
http://en.wikipedia.org/
http://www.ctio.noao.edu/~atokovin/tutorial/
VLTI General Description and tutorials
http://www.eso.org/projects/vlti/general/
ESO Instrumentation
http://www.eso.org/instruments/
Hubble Nuts & Bolts
http://hubblesite.org/sci.d.tech/nuts_.and._bolts/
๏ Modeling the MTF and Noise Characteristics of Complex Image Formation Systems,
Brian M. Bleeze (PhD Thesis):
http://www.cis.rit.edu/research/thesis/bs/1998/bleeze/thesis.html
๏ Modèles, estimation bayésienne et algorithmes pour la déconvolution d'images satellitaires et aériennes,
Chap. 2 & References, André Jalobeanu (PhD Thesis, French)
http://lsiit-miv.u-strasbg.fr/paseo/publis/j-these.pdf