A Bayesian approach to shape from coded aperture

Martinello, Manuel; Bishop, Tom E.; Favaro, Paolo

doi:10.1109/icip.2010.5653070

Cited by 5 publications

(8 citation statements)

References 13 publications

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“…Recently, methods able to deal with multimodal PSFs have been proposed in the context of extended depth of field with coded aperture (EDFCA) [1]. Most of these works, except [1,9], are dedicated to a particular PSF model, while we propose here a generic approach able to handle any kind of PSF shape, or even various PSF shapes in the same image, as encountered in multi-motion scenes.…”

Section: Related Workmentioning

confidence: 98%

“…[2] deals with single image motion blur identification, but is limited to image having only one moving object, contrarily to the approach proposed here. In [9], an EDFCA method is described. It is based on a marginalized likelihood which depends on scene parameters estimated directly from image data.…”

Section: Related Workmentioning

confidence: 99%

“…Our approach is more closely related to [2,9,10] where the PSF is selected locally thanks to a maximum likelihood criterion (ML). [2] deals with single image motion blur identification, but is limited to image having only one moving object, contrarily to the approach proposed here.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Single image local blur identification

Trouvé

Champagnat

Besnerais

et al. 2011

2011 18th IEEE International Conference on Image Processing

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International audienceWe present a new approach for spatially varying blur identification using a single image. Within each local patch in the image, the local blur is selected between a finite set of candidate PSFs by a maximum likelihood approach. We propose to work with a Generalized Likelihood to reduce the number of parameters and we use the Generalized Singular Value De- composition to limit the computing cost, while making proper image boundary hypotheses. The resulting method is fast and demonstrates good performance on simulated and real examples originating from applications such as motion blur identification and depth from defocus

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Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Single image local blur identification

Trouvé

Champagnat

Besnerais

et al. 2011

2011 18th IEEE International Conference on Image Processing

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show abstract

“…All the aperture patterns we consider in this work (top row) and their calibrated PSFs for two different blur scales (second and bottom row). (a) and (b) aperture masks used in both [13] and [43]; (c) annular mask used in [17]; (d) pattern proposed by [5]; (e) pattern proposed by [4]; (f) and (g) aperture masks used in [15]; (h) MURA pattern used in [10]. Fig.…”

Section: Coded Aperture Selectionmentioning

confidence: 99%

Single Image Blind Deconvolution with Higher-Order Texture Statistics

Martinello

Favaro

2011

Video Processing and Computational Video

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Abstract. We present a novel method for solving blind deconvolution, i.e., the task of recovering a sharp image given a blurry one. We focus on blurry images obtained from a coded aperture camera, where both the camera and the scene are static, and allow blur to vary across the image domain. As most methods for blind deconvolution, we solve the problem in two steps: First, we estimate the coded blur scale at each pixel; second, we deconvolve the blurry image given the estimated blur. Our approach is to use linear high-order priors for texture and second-order priors for the blur scale map, i.e., constraints involving two pixels at a time. We show that by incorporating the texture priors in a least-squares energy minimization we can transform the initial blind deconvolution task in a simpler optimization problem. One of the striking features of the simplified optimization problem is that the parameters that define the functional can be learned offline directly from natural images via singular value decomposition. We also show a geometrical interpretation of image blurring and explain our method from this viewpoint. In doing so we devise a novel technique to design optimally coded apertures. Finally, our coded blur identification results in computing convolutions, rather than deconvolutions, which are stable operations. We will demonstrate in several experiments that this additional stability allows the method to deal with large blur. We also compare our method to existing algorithms in the literature and show that we achieve state-of-the-art performance with both synthetic and real data.

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“…In [7,13,22,24,26,33,35,52] the authors use a temporally fixed and spatially varying mask in order to estimate the depth, and/or they refocus the out-of-focus part to get an always-in-focus (neat) image. In [16] the authors deal with the question of the optimal trade-off between depth of field and exposure time.…”

Section: Middle: the Image Reconstructed By Direct Deconvolution Rigmentioning

confidence: 99%

A Theory of Optimal Flutter Shutter for Probabilistic Velocity Models

Tendero¹,

Morel²

2016

SIAM J. Imaging Sci.

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Abstract. Flutter shutter (coded exposure) is a new paradigm for cameras that allows for an arbitrary increase of the exposure time when the relative camera/scene motion is uniform. The photon flux is interrupted according to a flutter shutter code. For arbitrarily severe uniform motion blur a well chosen code guarantees an invertible blur kernel. Yet, when the relative camera/scene velocity is a known constant, a flutter shutter cannot gain more than a 1.17 factor in terms of root mean-squared error compared to the optimal snapshot. In this paper, we prove that this optimality bound can be relaxed under the realistic assumption that a random model for the velocities is available. We give analytical formulae for the optimal flutter shutter code and the optimal snapshots associated with a random velocity distribution. Conversely we also prove formulae that reveal the velocity distribution underlying a given flutter shutter code. 1. Introduction. Digital cameras count at each pixel sensor the number of photons emitted by the observed scene during a time span called the exposure time. The photon count follows a Poisson random variable. Its mean is the ideal (noiseless) pixel value. The difference between this ideal pixel value and the observed sensor photon count is called (shot) noise. The ratio of the mean of the photon count over its standard-deviation is called the signal-to-noise ratio (SNR). In passive imaging systems there is no control over the scene lighting. Thus, the only safe way to increase the SNR is to integrate more photons by increasing the exposure time. Yet, when the scene or the camera moves during the exposure process, the resulting images are degraded by a motion blur. If the support of a motion blur kernel exceeds two pixels, the blur is no longer invertible. This limits the exposure time and therefore the image quality of a snapshot. A setup solving this photography trade-off between noise and blur was proposed in [3,4,6,37,38,39] for uniform camera-scene motions. The authors of these papers attached a flutter shutter to a camera to get an invertible motion blur kernel. A flutter shutter

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A Bayesian approach to shape from coded aperture

Cited by 5 publications

References 13 publications

Single image local blur identification

Single image local blur identification

Single Image Blind Deconvolution with Higher-Order Texture Statistics

A Theory of Optimal Flutter Shutter for Probabilistic Velocity Models

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