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Estimating an Image's Blur Kernel Using Natural Image Statistics, and Deblurring it: An Analysis of the Goldstein-Fattal Method

License: MIT License

CMake 0.12% C++ 34.49% C 65.39%

deblurring's Introduction

Deblurring

Estimating an Image's Blur Kernel Using Natural Image Statistics, and Deblurring it: An Analysis of the Goldstein-Fattal Method

Jérémy Anger, Gabriele Facciolo, Mauricio Delbracio

This program is part of the IPOL publication: http://www.ipol.im/pub/pre/211/

Version 20180630

Compilation:

run "cmake . && make" to produce an executable named "Deblurring"
requires a C++11 compatible compiler and the following libraries: libfftw3

Usage:

./Deblurring BLURRY_IMAGE KERNEL_SIZE KERNEL_OUTPUT DEBLURRED_OUTPUT [--alpha COMPENSATION_FACTOR=2.1]

- BLURRY_IMAGE: should be a hdr, png or jpeg file.
- KERNEL_SIZE: should be an odd integer large enough to contains the actual estimated kernel
- KERNEL_OUTPUT: output file for the estimated kernel, should be a .hdr in order to keep floating point values
- DEBLURRED_OUTPUT: output file for the deconvolved image (hdr or png), will have the same dynamic range as the input image.
- COMPENSATION_FACTOR: parameter alpha of the compensation filter, set it to 0 to disable the filtering
For more options, use "./Deblurring --help"

Example:

./Deblurring hollywood.jpg 15 kernel.hdr deblurred.png

Credits:

iio.c/h and conjugate_gradient.hpp: from https://github.com/mnhrdt/imscript
tvdeconv_20120607/: from http://www.ipol.im/pub/art/2012/g-tvdc/
args.hxx: from https://github.com/Taywee/args

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