DETECTING AND CORRECTING MOTION BLUR FROM IMAGES SHOT WITH CHANNEL-DEPENDENT EXPOSURE TIME
This article describes a pipeline developed to automatically detect and correct motion blur due to the airplane motion in aerial images provided by a digital camera system with channel-dependent exposure times. Blurred images show anisotropy in their Fourier Transform coefficients that can be dete...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2012-07-01
|
Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-3/341/2012/isprsannals-I-3-341-2012.pdf |
id |
doaj-b1f2a9c792984fbcabb9536674247d78 |
---|---|
record_format |
Article |
spelling |
doaj-b1f2a9c792984fbcabb9536674247d782020-11-25T01:07:59ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502012-07-01I-334134610.5194/isprsannals-I-3-341-2012DETECTING AND CORRECTING MOTION BLUR FROM IMAGES SHOT WITH CHANNEL-DEPENDENT EXPOSURE TIMEL. Lelégard0E. Delaygue1M. Brédif2B. Vallet3Université Paris Est, IGN, Laboratoire MATIS – 73 avenue de Paris, 94165 Saint-Mandé, FranceTecoptique – 180 rue du Genevois, 73000 Chambery, France – emeric.delaygue@cpe.frUniversité Paris Est, IGN, Laboratoire MATIS – 73 avenue de Paris, 94165 Saint-Mandé, FranceUniversité Paris Est, IGN, Laboratoire MATIS – 73 avenue de Paris, 94165 Saint-Mandé, FranceThis article describes a pipeline developed to automatically detect and correct motion blur due to the airplane motion in aerial images provided by a digital camera system with channel-dependent exposure times. Blurred images show anisotropy in their Fourier Transform coefficients that can be detected and estimated to recover the characteristics of the motion blur. To disambiguate the anisotropy produced by a motion blur from the possible spectral anisotropy produced by some periodic patterns present in a sharp image, we consider the phase difference of the Fourier Transform of two channel shot with different exposure times (i.e. with different blur extensions). This is possible because of the deep correlation between the three visible channels ensures phase coherence of the Fourier Transform coefficients in sharp images. In this context, considering the phase difference constitutes both a good detector and estimator of the motion blur parameters. In order to improve on this estimation, the phase difference is performed on local windows in the image where the channels are more correlated. The main lobe of the phase difference, where the phase difference between two channels is close to zero actually imitates an ellipse which axis ratio discriminates blur and which orientation and minor axis give respectively the orientation and the blur kernel extension of the long exposure-time channels. However, this approach is not robust to the presence in the phase difference of minor lobes due to phase sign inversions in the Fourier transform of the motion blur. They are removed by considering the polar representation of the phase difference. Based on the blur detection step, blur correction is eventually performed using two different approaches depending on the blur extension size: using either a simple frequency-based fusion for small blur or a semi blind iterative method for larger blur. The higher computing costs of the latter method make it only suitable for large motion blur, when the former method is not applicable.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-3/341/2012/isprsannals-I-3-341-2012.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
L. Lelégard E. Delaygue M. Brédif B. Vallet |
spellingShingle |
L. Lelégard E. Delaygue M. Brédif B. Vallet DETECTING AND CORRECTING MOTION BLUR FROM IMAGES SHOT WITH CHANNEL-DEPENDENT EXPOSURE TIME ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
L. Lelégard E. Delaygue M. Brédif B. Vallet |
author_sort |
L. Lelégard |
title |
DETECTING AND CORRECTING MOTION BLUR FROM IMAGES SHOT WITH CHANNEL-DEPENDENT EXPOSURE TIME |
title_short |
DETECTING AND CORRECTING MOTION BLUR FROM IMAGES SHOT WITH CHANNEL-DEPENDENT EXPOSURE TIME |
title_full |
DETECTING AND CORRECTING MOTION BLUR FROM IMAGES SHOT WITH CHANNEL-DEPENDENT EXPOSURE TIME |
title_fullStr |
DETECTING AND CORRECTING MOTION BLUR FROM IMAGES SHOT WITH CHANNEL-DEPENDENT EXPOSURE TIME |
title_full_unstemmed |
DETECTING AND CORRECTING MOTION BLUR FROM IMAGES SHOT WITH CHANNEL-DEPENDENT EXPOSURE TIME |
title_sort |
detecting and correcting motion blur from images shot with channel-dependent exposure time |
publisher |
Copernicus Publications |
series |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
2194-9042 2194-9050 |
publishDate |
2012-07-01 |
description |
This article describes a pipeline developed to automatically detect and correct motion blur due to the airplane motion in aerial
images provided by a digital camera system with channel-dependent exposure times. Blurred images show anisotropy in their Fourier
Transform coefficients that can be detected and estimated to recover the characteristics of the motion blur. To disambiguate the
anisotropy produced by a motion blur from the possible spectral anisotropy produced by some periodic patterns present in a sharp
image, we consider the phase difference of the Fourier Transform of two channel shot with different exposure times (i.e. with
different blur extensions). This is possible because of the deep correlation between the three visible channels ensures phase
coherence of the Fourier Transform coefficients in sharp images. In this context, considering the phase difference constitutes both a
good detector and estimator of the motion blur parameters. In order to improve on this estimation, the phase difference is performed
on local windows in the image where the channels are more correlated. The main lobe of the phase difference, where the phase
difference between two channels is close to zero actually imitates an ellipse which axis ratio discriminates blur and which orientation
and minor axis give respectively the orientation and the blur kernel extension of the long exposure-time channels. However, this
approach is not robust to the presence in the phase difference of minor lobes due to phase sign inversions in the Fourier transform of
the motion blur. They are removed by considering the polar representation of the phase difference. Based on the blur detection step,
blur correction is eventually performed using two different approaches depending on the blur extension size: using either a simple
frequency-based fusion for small blur or a semi blind iterative method for larger blur. The higher computing costs of the latter
method make it only suitable for large motion blur, when the former method is not applicable. |
url |
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-3/341/2012/isprsannals-I-3-341-2012.pdf |
work_keys_str_mv |
AT llelegard detectingandcorrectingmotionblurfromimagesshotwithchanneldependentexposuretime AT edelaygue detectingandcorrectingmotionblurfromimagesshotwithchanneldependentexposuretime AT mbredif detectingandcorrectingmotionblurfromimagesshotwithchanneldependentexposuretime AT bvallet detectingandcorrectingmotionblurfromimagesshotwithchanneldependentexposuretime |
_version_ |
1725184921699352576 |