journal club bozeman 6 november 2001
DESCRIPTION
Journal Club Bozeman 6 November 2001. Population: 636 800 Ladies: 337 200 Gents: 299 600. SXT image formation process. BLUR. i true brightness distribution. p point spread function. c convolution of i and p. - PowerPoint PPT PresentationTRANSCRIPT
Journal Club
Bozeman
6 November 2001
Population: 636 800 Ladies: 337 200
Gents: 299 600
SXT image formation process
itrue brightness
distribution
ppoint spread
function
cconvolution of i and p
BLUR
dblurred & noisy
data
d = c + noise
NOISE
cconvolution of i and p
SXT image formation process (continued)
SXT image formation process (continued)
What we know is d. For i, p, c we can only look for approximations.
cross sections
i
d
Deconvolution
To find i from d and p
or
To find p from d and i
i – approximation of image (true brightness distribution)
p – approximation of point spread function
d – data (blurred & noisy)
Blind Deconvolution
i – image (true brightness distribution)
p – point spread function
d – data (blurred & noisy)
Find new estimate
of i from d and p
Find new estimate
of p from d and i
Find new estimate
of I from D and P
Find new estimate
of P from D and I
I, P, D – Fourier transforms of i, d, p
d = convolution of p and i
+ noise
D = product of P and I
+ noise in fouries space
PURPOSE
To determine the approximation of the core part of the SXT PSF from flare observations collected in-flight, in thick aluminum filter.
STEPS TO ACHIVE
• Select appropriate SXT data set
• Find first approximation of the PSF by Steepest Descent Method
• Improve it by Blind Deconvolution
SXT Point Spread Function Model
Two regions
spiky core
extended wings
Large flare image taken on 27 Feb 1992 at 09:51.
Half resolution SXT data.
Wing part of SXT PSF
Core part of SXT PSF
Ground calibration images in Al-K line (White Sands 1991)
Elliptical distortion of SXT PSF (strong in CCD corners)Contours at 0.1, 0.2, 0.4 and 0.8 of maximum value.
To the left, a coverage map of the CCD detector surface by full resolution SXT frames. Gray intensity says how many times a given pixel was captured within a full resolution frame during year 2000. To the right a shaded surface for the coverage map (Log10 scale).
Where SXT Data have been taken during the year 2000
Compact source images selected in thick aluminium filter data (small dots) and WSMR calibration beam positions on CCD surface (crosses).
• Compact • CCD Temperature below –20o C• DC below 50 DN• Taken outside SAA• Global maximum present at least 7 pixels away from image boundaries• Not saturated but maximum value above 1000 DN
Selection of SXT data
Steepest Descent Method
PSF is the sharpest object of photon origins that can be formed on SXT CCD
Find sequence of images placed nearly at the same location on CCD
• • •
Steepest Descent Method (continued)
• • •
• • •
Normalize image signal in certain sub-arrays centered at the peak. (here 15x15 square sub-arrays)
image sequence
sub-array sequencenormalized
Steepest Descent Method (continued)
• • •
Construct PSF approximation by taking at each pixel minimal signal value possible to find in the whole normalized sequence
at respective pixel position.
sub-array sequencenormalized
Calibration Steepest descents
x cross-section y cross-section
Steepest Descent Method – comparison with WSMR calibration data
Initial data preparation for Blind Deconvolution
Construct initial guess for PSF by steepest descent method and put it into an image size array
Select the most compact flare image found in the neighborhood of a
givenCCD pixel
Fourier transformsPSF
SXT flare mage
Re
Re Im
Im
Find new estimate
of I2 from D and P2
Find new estimate
of P1 from D and I1
Impose image constraints
• positivity
• conservation of total counts
Fourier Transform
Inverse Fourier Transform
Impose PSF constraints
• positivity
• normalization
Fourier Transform
Inverse Fourier Transform
I2
i2
i1
I1
P1
p1
p2
P2
Fourier domain constraint
average I1 and I2
Fourier transform of Initial approximation
for PSF
ALGORITHM
RESULTS
Input PSF steepest descents
Restored PSF blind deconvolution
RESULTS
Input SXT data Restored SXT data
RESULTS
Input SXT data Restored SXT data
Conclusions
Blind Deconvolution of the selected SXT flare data can give us:
• Sharper PSF core profile than can be directly obtained from data by steepest descent method
• Peak sharpening in SXT data
• Peak separation in data
• Works fast
What we have done
• Working IDL code
• Good SXT Flare data selected
• First deconvolutions of SXT data performed
Will do next
• Improve the code
• Fit deconvolved PSFs by Moffat functions
• Prepare web site about the project (partly done) to make the code accessible for other users.
• Add the code to Solar Soft package