ui.filter_with_mrfilter¶
Denoise FITS images with Wavelet Transform.
This script use mr_filter – a program written CEA/CosmoStat (www.cosmostat.org) – to make Wavelet Transform.
Usage¶
filter_with_mrfilter.py [-h] [--type-of-filtering INTEGER]
[--coef-detection-method INTEGER]
[--type-of-multiresolution-transform INTEGER]
[--type-of-filters INTEGER]
[--type-of-non-orthog-filters INTEGER]
[--noise-model INTEGER]
[--number-of-scales integer]
[--k-sigma-noise-threshold FLOAT]
[--number-of-iterations integer] [--epsilon FLOAT]
[--support-file-name FILE]
[--suppress-isolated-pixels]
[--kill-isolated-pixels] [--suppress-last-scale]
[--detect-only-positive-structure]
[--precision FLOAT]
[--first-detection-scale INTEGER]
[--suppress-positivity-constraint]
[--maximum-level-constraint]
[--mask-file-path MASK_FILE_NAME]
[--offset-after-calibration FLOAT]
[--correction-offset]
[--input-image-scale INPUT_IMAGE_SCALE]
[--noise-cdf-file FILE] [--tmp-dir DIRECTORY]
[--verbose] [--debug] [--max-images INTEGER]
[--telid INTEGER] [--eventid INTEGER]
[--camid STRING] [--benchmark STRING]
[--label STRING] [--plot] [--saveplot FILE]
[--output FILE]
FILE [FILE ...]
Denoise images with Wavelet Transform.
positional arguments:
FILE The files image to process. If fileargs is a
directory, all files it contains are processed.
optional arguments:
-h, --help show this help message and exit
--type-of-filtering INTEGER, -f INTEGER
Type of filtering: 1: Multiresolution Hard K-Sigma
Thresholding 2: Multiresolution Soft K-Sigma
Thresholding 3: Iterative Multiresolution Thresholding
4: Adjoint operator applied to the multiresolution
support 5: Bivariate Shrinkage 6: Multiresolution
Wiener Filtering 7: Total Variation + Wavelet
Constraint 8: Wavelet Constraint Iterative Methods 9:
Median Absolute Deviation (MAD) Hard Thesholding 10:
Median Absolute Deviation (MAD) Soft Thesholding.
Default=1.
--coef-detection-method INTEGER, -C INTEGER
Coef_Detection_Method: 1: K-SigmaNoise Threshold 2:
False Discovery Rate (FDR) Theshold 3: Universal
Threshold 4: SURE Threshold 5: Multiscale SURE
Threshold. Default=1.
--type-of-multiresolution-transform INTEGER, -t INTEGER
Type of multiresolution transform: 1: linear wavelet
transform: a trous algorithm 2: bspline wavelet
transform: a trous algorithm 3: wavelet transform in
Fourier space 4: morphological median transform 5:
morphological minmax transform 6: pyramidal linear
wavelet transform 7: pyramidal bspline wavelet
transform 8: pyramidal wavelet transform in Fourier
space: algo 1 (diff. between two resolutions) 9:
Meyer's wavelets (compact support in Fourier space)
10: pyramidal median transform (PMT) 11: pyramidal
laplacian 12: morphological pyramidal minmax transform
13: decomposition on scaling function 14: Mallat's
wavelet transform (7/9 filters) 15: Feauveau's wavelet
transform 16: Feauveau's wavelet transform without
undersampling 17: Line Column Wavelet Transform
(1D+1D) 18: Haar's wavelet transform 19: half-
pyramidal transform 20: mixed Half-pyramidal WT and
Median method (WT-HPMT) 21: undecimated diadic wavelet
transform (two bands per scale) 22: mixed WT and PMT
method (WT-PMT) 23: undecimated Haar transform: a
trous algorithm (one band per scale) 24: undecimated
(bi-) orthogonal transform (three bands per scale) 25:
non orthogonal undecimated transform (three bands per
scale) 26: Isotropic and compact support wavelet in
Fourier space 27: pyramidal wavelet transform in
Fourier space: algo 2 (diff. between the square of two
resolutions) 28: Fast Curvelet Transform. Default=2.
--type-of-filters INTEGER, -T INTEGER
Type of filters: 1: Biorthogonal 7/9 filters 2:
Daubechies filter 4 3: Biorthogonal 2/6 Haar filters
4: Biorthogonal 2/10 Haar filters 5: Odegard 9/7
filters 6: 5/3 filter 7: Battle-Lemarie filters (2
vanishing moments) 8: Battle-Lemarie filters (4
vanishing moments) 9: Battle-Lemarie filters (6
vanishing moments) 10: User's filters 11: Haar filter
12: 3/5 filter 13: 4/4 Linar spline filters 14:
Undefined sub-band filters. Default=1.
--type-of-non-orthog-filters INTEGER, -U INTEGER
Type of non-orthogonal filters: 1: SplineB3-Id+H:
H=[1,4,6,4,1]/16, Ht=H, G=Id-H, Gt=Id+H 2:
SplineB3-Id: H=[1,4,6,4,1]/16, Ht=H, G=Id-H*H, Gt=Id
3: SplineB2-Id: H=4[1,2,1]/4, Ht=H, G=Id-H*H, Gt=Id 4:
Harr/Spline POS:
H=Haar,G=[-1/4,1/2,-1/4],Ht=[1,3,3,1]/8,Gt=[1,6,1]/4.
Default=2.
--noise-model INTEGER, -m INTEGER
Noise model: 1: Gaussian noise 2: Poisson noise 3:
Poisson noise + Gaussian noise 4: Multiplicative noise
5: Non-stationary additive noise 6: Non-stationary
multiplicative noise 7: Undefined stationary noise 8:
Undefined noise 9: Stationary correlated noise 10:
Poisson noise with few events. Default=1.
--number-of-scales integer, -n integer
Number of scales used in the multiresolution
transform. Default=4.
--k-sigma-noise-threshold FLOAT, -s FLOAT
Thresholding at nsigma * SigmaNoise. Default=3.
--number-of-iterations integer, -i integer
Maximum number of iterations. Default=10.
--epsilon FLOAT, -e FLOAT
Convergence parameter. Default=0.001000 or 0.000010 in
case of poisson noise with few events.
--support-file-name FILE, -w FILE
Creates an image from the multiresolution support and
save to disk.
--suppress-isolated-pixels, -k
Suppress isolated pixels in the support
--kill-isolated-pixels
Suppress isolated pixels in the support (scipy
implementation)
--suppress-last-scale, -K
Suppress the last scale (to have background pixels =
0)
--detect-only-positive-structure, -p
Detect only positive structure
--precision FLOAT, -E FLOAT
Epsilon = precision for computing thresholds (only
used in case of poisson noise with few events).
Default=1.00e-03.
--first-detection-scale INTEGER, -F INTEGER
First scale used for the detection. Default=1.
--suppress-positivity-constraint, -P
Suppress positivity constraint
--maximum-level-constraint
Add the maximum level constraint. Max value is 255.
--mask-file-path MASK_FILE_NAME
Filename of the mask containing the bad data
(Mask[i,j]=1 for good pixels and 0 otherwise. Default
is none.
--offset-after-calibration FLOAT
Value added to all pixels of the input image after
calibration. Default=0.
--correction-offset Apply a correction offset to the output image.
--input-image-scale INPUT_IMAGE_SCALE
Use a different scale for the input image ('linear',
'log' or 'sqrt'). Default='linear'.
--noise-cdf-file FILE
The JSON file containing the Cumulated Distribution
Function of the noise model used to inject artificial
noise in blank pixels (those with a NaN value).
Default=None.
--tmp-dir DIRECTORY The directory where temporary files are written.
--verbose, -v Verbose mode
--debug Debug mode
--max-images INTEGER The maximum number of images to process
--telid INTEGER Only process images from the specified telescope
--eventid INTEGER Only process images from the specified event
--camid STRING Only process images from the specified camera
--benchmark STRING, -b STRING
The benchmark method to use to assess the algorithm
for thegiven images
--label STRING, -l STRING
The label attached to the produced results
--plot Plot images
--saveplot FILE The output file where to save plotted images
--output FILE, -o FILE
The output file path (JSON)
Examples
./filter_with_mrfilter.py -h ./filter_with_mrfilter.py ./test.fits ipython3 – ./filter_with_mrfilter.py -n4 ./test.fits
Notes
This script requires the mr_filter program (http://www.cosmostat.org/software/isap/).
-
pywi.ui.filter_with_mrfilter.add_arguments(parser)[source]¶ Populate the given argparse.ArgumentParser with arguments.
This function can be used to make the definition these argparse arguments reusable in other modules and avoid the duplication of these definitions among the executable scripts.
Parameters: parser (argparse.ArgumentParser) – The parser to populate. Returns: Return the populated ArgumentParser object. Return type: argparse.ArgumentParser