Multiple-image deblurring with spatially-variant point spread functions

Vio, R.; Nagy, James G.; Wamsteker, W.

doi:10.1051/0004-6361:20035754

Cited by 6 publications

(2 citation statements)

References 20 publications

(33 reference statements)

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“…Previous work on non-uniform deblurring has focused on piecewise-uniform blurs arising from multiple moving objects in the scene [7,12], spatially varying combinations of localized uniform blurs [17,27], or blur arising from rotations of planar objects in the scene [24]. However, apart from Sawchuk [22], who assumes a known transformation, these approaches generally rely on the assumption that the blur is locally uniform, and do not consider global models for continuously varying blur, such as those arising from arbitrary rotations of the camera about its optical center during exposure, as modelled in our work.…”

Section: Related Workmentioning

confidence: 99%

Non-uniform Deblurring for Shaken Images

et al. 2011

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Blur from camera shake is mostly due to the 3D rotation of the camera, resulting in a blur kernel that can be significantly non-uniform across the image. However, most current deblurring methods model the observed image as a convolution of a sharp image with a uniform blur kernel. We propose a new parametrized geometric model of the blurring process in terms of the rotational velocity of the camera during exposure. We apply this model to two different algorithms for camera shake removal: the first one uses a single blurry image (blind deblurring), while the second one uses both a blurry image and a sharp but noisy image of the same scene. We show that our approach makes it possible to model and remove a wider class of blurs than previous approaches, including uniform blur as a special case, and demonstrate its effectiveness with experiments on real images.

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Section: Related Workmentioning

confidence: 99%

Non-uniform Deblurring for Shaken Images

et al. 2011

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“…Note that this model can be computed very efficiently by computing the discrete Fourier transforms of each patch and filter using the fast Fourier transform (FFT), multiplying them element-wise in the frequency domain, and then taking the inverse discrete Fourier transform of the result. Under varying assumptions, different authors have also proposed locally-uniform models of spatially-variant blur, which take similar forms to Equation (1.21) (Nagy & O'Leary 1998, Vio, Nagy, Tenorio & Wamsteker 2005, Tai, Du, Brown & Lin 2010.…”

Section: 51mentioning

confidence: 99%

Efficient, blind, spatially-variant deblurring for shaken images

et al. 2014

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In this chapter we discuss modeling and removing spatially-variant blur from photographs. We describe a compact global parameterization of camera shake blur, based on the 3D rotation of the camera during the exposure. Our model uses three-parameter homographies to connect camera motion to image motion and, by assigning weights to a set of these homographies, can be seen as a generalization of the standard, spatially-invariant convolutional model of image blur. As such we show how existing algorithms, designed for spatially-invariant deblurring, can be "upgraded" in a straightforward manner to handle spatially-variant blur instead. We demonstrate this with algorithms working on real images, showing results for blind estimation of blur parameters from single images, followed by non-blind image restoration using these parameters. Finally, we introduce an efficient approximation to the global model, which significantly reduces the computational cost of modeling the spatially-variant blur. By approximating the blur as locally-uniform, we can take advantage of fast Fourier-domain convolution and deconvolution, reducing the time required for blind deblurring by an order of magnitude.

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