Motion Blur Identification Based on Differently Exposed Images

Tico, Marius; Trimeche, Mejdi; Vehviläinen, Markku

doi:10.1109/icip.2006.312843

Cited by 29 publications

(25 citation statements)

References 7 publications

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“…This method isn't suitable for images shot with different channels. An iterative approach is proposed by (Tico et al, 2007) where the PSF estimation and the image deconvolution are performed at the same time. The initial PSF is a rough estimation which is refine by repeating the operation on the roughly deblurred image.…”

Section: Multi-image Methodsmentioning

confidence: 99%

Detecting and Correcting Motion Blur From Images Shot With Channel-Dependent Exposure Time

Lelégard¹,

Delaygue²,

Brédif³

et al. 2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT: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.

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Section: Multi-image Methodsmentioning

confidence: 99%

Detecting and Correcting Motion Blur From Images Shot With Channel-Dependent Exposure Time

Lelégard¹,

Delaygue²,

Brédif³

et al. 2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…Deblurring images in software using these methods [4] can reduce both spatially invariant [5][6][7][8] and spatially variant (SV) [9][10][11][12] motion blur. Multiple images with different exposures can be used for this purpose, again for both space-invariant [13,14] and space-variant blur [15]. In recent years, many approaches were proposed that facilitate blind deconvolution using various alternative optical designs, such as coded aperture [16,17] or wavefront coding [18].…”

Section: Introductionmentioning

confidence: 99%

Platform motion blur image restoration system

Olivas

Šorel

Nikzad

et al. 2012

Imaging and Applied Optics Technical Papers

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Platform motion blur is a common problem for airborne and space-based imagers. Photographs taken by hand or from moving vehicles in low-light conditions are also typically blurred. Correcting image motion blur poses a formidable problem since it requires a description of the blur in the form of the point spread function (PSF), which in general is dependent on spatial location within the image. Here we introduce a computational imaging system that incorporates optical position sensing detectors (PSDs), a conventional camera, and a method to reconstruct images degraded by spatially variant platform motion blur. A PSD tracks the movement of light distributions on its surface. It leverages more energy collection than a single pixel since it has a larger area making it proportionally faster. This affords it high temporal resolution as it measures the PSF at a specific location in the image field. Using multiple PSDs, a spatially variant PSF is generated and used to reconstruct images.

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“…Another way to estimate the PSF has been proposed in Tico et al (2006); Tico & Vehviläinen (2007a); Yuan et al (2007), where a second image of the scene is taken with a short exposure. Although noisy, the secondary image is much less affected by motion blur and it can be used as a reference for estimating the motion blur PSF which degraded the principal image.…”

Section: Introductionmentioning

confidence: 99%