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View Code? Open in Web Editor NEWNon-parametric sub-pixel local point spread function estimation
License: GNU Affero General Public License v3.0
Non-parametric sub-pixel local point spread function estimation
License: GNU Affero General Public License v3.0
Point Spread Function Estimation from a Random Target ====================================================== Version 1.3 - December 26, 2011 (see list of BUGS) by Mauricio Delbracio <[email protected]> Pablo Muse Andres Almansa Introduction ----------- Extrinsic image blur can be observed when the camera's focal distance is not correctly adjusted by the user, when the objects in the scene appear at different depths, or when the relative motion between the camera and the scen is faster than the shutter speed (motion blur). Besides these sources of blur, even in ideal acquisition conditions there is a permanent intrinsic physical camera blur due to light diffraction, sensor resolution, lens aberration, and anti-aliasing filters. Our goal here is to accurately estimate the Point Spread Function - PSF, that models the intrinsic camera blur. This function can be locally interpreted as the response of the camera to a point light source. In [1] we presented a theoretical study proving that the sub-pixel PSF estimation problem is well-posed even with a single image capture, as long as the captured scene is well chosen. Indeed, theoretical bounds show that a near-optimal accuracy can be achieved by taking a single snapshot of a calibration pattern mimicking a Bernoulli(0.5) white noise. We first use an algorithm to accurately estimate the pattern position and its illumination conditions. This allows for accurate geometric registration and radiometric correction; Once these procedures have been applied, the local PSF can be directly computed by inverting a linear system that is well-posed and consequently its inversion does not require any regularization or prior model. Files ----- COPYING Makefile README.txt VERSION lsd.c lsd.h thin_plates.c thin_plates.h image.c image.h io_pgm.c io_phm.h detect_pattern.c detect_pattern.h psf_estim.c psf_estim.h psf_estim_main.c nnls.c nnls.h pattern_noise.pgm img_example.pgm doxygen.config Requirements ------------ - The fftw3 header and libraries are required on the system for compilation and execution. See http://www.fftw.org/ - The cblas header and libraries are required on the system for compilation and execution. - The lapack library is required on the system for compilation and execution. Compilation ----------- Simply use the provided Makefile, with the command `make`. You need to set the directory where the libraries: ffw3, cblas and lapack have the respective header and libraries files. Running ------- Usage: ./psf_estim [options] <input file> <output file> Only PGM 16/8 bits images are supported. Options: -s <number> The super-resolution factor, positive integer (default 4) -k <number> PSF support size (default 4s+1) -o <file> Estimated PSF saved to a 8bits PGM image -p <file> Noise Pattern PGM image -d <file> Save the input image with detected pattern marked (24bits PPM) -t <0,1,2> Least Squares solver: 0 - Least Squares 1 - Least Squares + thresholding (default) 2 - Non-negative least Squares (slowest) Parameter Explanation -s 'number' : The superresolution factor, i.e. how many additional samples per observed pixel will be computed. (default 4) -k 'number' : The support size in the superresolved grid. For very sharp images 4s+1 should be enough. (default 4s + 1) -o 'filename' : Save the estimated PSF as an 8-bit image file. Values are rescaled to max_value be 255. Just for visualization purposes. -p 'filename' : Input Noise Pattern PGM Image. (default pattern_noise.pgm) -d 'filename' : Save the input image into a PGM image file 'filename' with marks where the PSF pattern has been detected. -t <0,1,2> : Choose the numerical algorithm for solving Least Squares. 0 - Least Squares 1 - Least Squares + thresholding (default) 2 - Non-negative least Squares (slowest) <input file> : Input 8/16 bits PGM Image <output file> : Output PSF written into a TXT file as a k x k matrix of floats Example ./psf_estim img_example.pgm psf.txt Documentation ------------- The following is an implementation of the Point Spread Function Estimation Algorithm presented in: [1] M. Delbracio, P. Muse, A. Almansa and J.M. Morel. The non-parametric sub-pixel local point spread function estimation is a well posed problem. Submitted to the International Journal of Computer Vision (IJCV), November 2010. and in more detail described on the portal IPOL www.ipol.im where there is much more information, including this code and an online demo version: http://www.ipol.im/pub/algo/admm_non_blind_psf_estimation/ Please report bugs in psf_estim to <[email protected]>. BUGS / HISTORY -------------- Changes in Version 1.3 --> indentation fixed. TABs replaced by four spaces Changes in Version 1.2 --> indentation fixed to four spaces --> style code was changed according to reviewers from the IPOL journal Changes in Version 1.1 --> bicubic parameter 'a' was wrongly set to 0.5 instead of -0.5. --> thin-plate regularization parameter is from [0,Inf) instead of [0,1] --> thin-plate regularization parameter is set to 10 (instead of 0.1) --> order of parameters nx and ny in 'fftwf_plan_r2r_2d' where in reverse order. First should be ny then nx (as we work in Raw major) Copyright and License --------------------- "Point Spread Function Estimation from a Random Target" Copyright 2010-2011 mauricio delbracio ([email protected]) This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details. You should have received a copy of the GNU Affero General Public License along with this program. If not, see <http://www.gnu.org/licenses/>.
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