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185
Coded Aperture Pairs for Depth from Defocus and Defocus Deblurring
 International Journal on Computer Vision
, 2011
"... The classical approach to depth from defocus uses two images taken with circular apertures of different sizes. We show in this paper that the use of a circular aperture severely restricts the accuracy of depth from defocus. We derive a criterion for evaluating a pair of apertures with respect to the ..."
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Cited by 37 (6 self)
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The classical approach to depth from defocus uses two images taken with circular apertures of different sizes. We show in this paper that the use of a circular aperture severely restricts the accuracy of depth from defocus. We derive a criterion for evaluating a pair of apertures with respect
Motion Deblurring as Optimisation
"... Motion blur is one of the most common causes of image degradation. It is of increasing interest due to the deep penetration of digital cameras into consumer applications. In this paper, we start with a hypothesis that there is sufficient information within a blurred image and approach the deblurring ..."
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the deblurring problem as an optimisation process where the deblurring is to be done by satisfying a set of conditions. These conditions are derived from first principles underlying the degradation process assuming noisefree environments. We propose a novel but effective method for removing motion blur from a
Negative Results for Multilevel Preconditioners in Image Deblurring
 In Proc. Scale Space
, 1999
"... . A onedimensional deconvolution problem is discretized and certain multilevel preconditioned iterative methods are applied to solve the resulting linear system. The numerical results suggest that multilevel multiplicative preconditioners may have no advantage over twolevel multiplicative precondi ..."
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Cited by 6 (0 self)
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preconditioners. In fact, in the numerical experiments they perform worse than comparable twolevel preconditioners. 1 Introduction In image deblurring, the goal is to estimate the true image u true from noisy, blurred data z(x) = Z k(x \Gamma y) u true (y) dy + j(x): (1) Here j represents noise
Analysis of Coded Apertures for Defocus Deblurring of HDR Images
"... In recent years, research on computational photography has reached important advances in the field of coded apertures for defocus deblurring. These advances are known to perform well for low dynamic range images (LDR), but nothing is written about the extension of these techniques to high dynamic ra ..."
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range imaging (HDR). In this paper, we focus on the analysis of how existing coded apertures techniques perform in defocus deblurring of HDR images. We present and analyse three different methods for recovering focused HDR radiances from an input of blurred LDR exposures and from a single blurred HDR
Solution of Linear Systems Arising in Nonlinear Image Deblurring
 in Workshop on Scientific Computing
, 1997
"... . This paper deals with the solution of large linear systems which arise when certain optimization methods are applied in image deblurring. An unconstrained penalized least squares minimization problem with total variation penalty is considered. Also addressed are several penalized least squares pro ..."
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Cited by 4 (0 self)
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, blurred twodimensional images z ij = Z Z \Omega k(x i \Gamma x 0 ; y j \Gamma y 0 ) u(x 0 ; y 0 ) dx 0 dy 0 + ffl ij (1) def = (Ku)(x i ; y j ) + ffl ij ; 1 i n x ; 1 j n y ; where\Omega is the unit square in IR 2 . Our goal is to estimate the true image u, given the observed data
To Denoise or Deblur: Parameter Optimization for Imaging Systems
"... In recent years smartphone cameras have improved a lot but they still produce very noisy images in low light conditions. This is mainly because of their small sensor size. Image quality can be improved by increasing the aperture size and/or exposure time however this make them susceptible to defocus ..."
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to defocus and/or motion blurs. In this paper, we analyze the tradeoff between denoising and deblurring as a function of the illumination level. For this purpose we utilize a recently introduced framework for analysis of computational imaging systems that takes into account the effect of (1) optical
Preconditioned Landweber method for spaceinvariant image deblurring
, 2004
"... The Landweber method is the simplest iterative regularizing algorithm for solving linear illposed problems of the form Af = g; it is characterized by the recurrence fk+1 = fk + τA ∗ (g − Afk), where A ∗ is the adjoint of the blurring operator A, τ is a parameter controlling the convergence and the ..."
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The Landweber method is the simplest iterative regularizing algorithm for solving linear illposed problems of the form Af = g; it is characterized by the recurrence fk+1 = fk + τA ∗ (g − Afk), where A ∗ is the adjoint of the blurring operator A, τ is a parameter controlling the convergence
Iterative Restoration Deblurring of SPOT Panchromatic Images
"... This report describes the results of two preliminary tests. One was a test with a previously reported constrained iterative restoration technique (Schaefer and others, 1981, Kawata and Ichioka, 1980) to deblur 10mresolution panchromatic spectral band images from the SPOT1, HRV 2 sensor system. 1 ..."
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This report describes the results of two preliminary tests. One was a test with a previously reported constrained iterative restoration technique (Schaefer and others, 1981, Kawata and Ichioka, 1980) to deblur 10mresolution panchromatic spectral band images from the SPOT1, HRV 2 sensor system. 1
Application of Blind Deblurring Algorithm for Iris
"... Iris recognition is a form of biometric technology that authenticates individuals by using the unique iris patterns between the pupil and the sclera. There are three factors: Defocus, Motion Blur, and OffAngle to substantially degrade performance more than the other quality. The work described in t ..."
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in this paper is interested in Motion Blur. The iris image will appear blurry which can reduce iris recognition accuracy. The focus of the article is to achieve a quality edge preserving image restoration using Total Variation (TV)L1 regularization technique. L1 norm based approaches do not penalize edges
Rotational Motion Deblurring of a Rigid Object from a Single Image ∗
"... Most previous motion deblurring methods restore the degraded image assuming a shiftinvariant linear blur filter. These methods are not applicable if the blur is caused by spatially variant motions. In this paper, we model the physical properties of a 2D rigid body movement and propose a practical ..."
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Most previous motion deblurring methods restore the degraded image assuming a shiftinvariant linear blur filter. These methods are not applicable if the blur is caused by spatially variant motions. In this paper, we model the physical properties of a 2D rigid body movement and propose a practical
Results 11  20
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185