Depth From Defocus: A Real Aperture Imaging Approach

Chaudhuri, Subhasis; Rajagopalan, A. N.; Pentland, Alex

doi:10.1007/978-1-4612-1490-8

Cited by 183 publications

(136 citation statements)

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“…Depth From Defocus (DFD): Focus and defocus analysis for depth estimation has significant advantages over stereo and structure from motion as shown by [19,14,4], since they circumvent the correspondence problem. Another advantage of DFD over stereo is that only a single camera is required in DFD.…”

Section: Prior Workmentioning

confidence: 99%

Variable focus video: Reconstructing depth and video for dynamic scenes

Shroff

Veeraraghavan

Taguchi

et al. 2012

2012 IEEE International Conference on Computational Photography (ICCP)

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Traditional depth from defocus (DFD) algorithms assume that the camera and the scene are static during acquisition time. In this paper, we examine the effects of camera and scene motion on DFD algorithms. We show that, given accurate estimates of optical ow (OF), one can robustly warp the focal stack (FS) images to obtain a virtual static FS and apply traditional DFD algorithms on the static FS. Acquiring accurate OF in the presence of varying focal blur is a challenging task. We show how defocus blur variations cause inherent biases in the estimates of optical ow. We then show how to robustly handle these biases and compute accurate OF estimates in the presence of varying focal blur. This leads to an architecture and an algorithm that converts a traditional 30 fps video camera into a co-located 30 fps image and a range sensor. Further, the ability to extract image and range information allows us to render images with artistic depth of eld effects, both extending and reducing the depth of eld of the captured images. We demonstrate experimental results on challenging scenes captured using a camera prototype. Computational Photography (ICCP), 2012 IEEE International Conference onThis work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.

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Section: Prior Workmentioning

confidence: 99%

Variable focus video: Reconstructing depth and video for dynamic scenes

Shroff

Veeraraghavan

Taguchi

et al. 2012

2012 IEEE International Conference on Computational Photography (ICCP)

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“…Defocus blur has been well-studied in the computer vision literature, particularly as it relates to 3D reconstruction [12,3]. Much of the early literature is based on the shift-invariant model of blur, which is only applicable in regions of an image corresponding to equiblur surfaces.…”

Section: Related Workmentioning

confidence: 99%

Removing Partial Occlusion from Blurred Thin Occluders

McCloskey

Langer

Siddiqi

2010

2010 20th International Conference on Pattern Recognition

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We present a method to remove partial occlusion that arises from out-of-focus thin foreground occluders such as wires, branches, or a fence. Such partial occlusion causes the irradiance at a pixel to be a weighted sum of the radiances of a blurred foreground occluder and that of the background. The result is that the background component has lower contrast than it would if seen without the occluder. In order to remove the contribution of the foreground in such regions, we characterize the position and size of the occluder in a narrow aperture image. In subsequent images with wider apertures, we use this characterization to remove the contribution of the foreground, thereby restoring contrast in the background. We demonstrate our method on real camera images without assuming that the background is static.

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“…Consider the image formation process in a real aperture camera employing a thin lens [3]. When a point light source is in focus, all light rays that are radiated from the object point and intercepted by the lens converge at a point on the image plane.…”

Section: Defocus As a Diffusion Processmentioning

confidence: 99%

“…Subsequently, there has been substantial work done using deterministic [6] and statistical techniques [3] in the spatial domain and also by solving in the frequency domain [14]. However, most of the works done assume that the observations do not suffer from self-occlusion.…”

Section: Introductionmentioning

confidence: 99%

Shape Recovery Using Stochastic Heat Flow

Namboodiri

Chaudhuri

2007

Procedings of the British Machine Vision Conference 2007

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We consider the problem of depth estimation from multiple images based on the defocus cue. For a Gaussian defocus blur, the observations can be shown to be the solution of a deterministic but inhomogeneous diffusion process. However, the diffusion process does not sufficiently address the case in which the Gaussian kernel is deformed. This deformation happens due to several factors like self-occlusion, possible aberrations and imperfections in the aperture. These issues can be solved by incorporating a stochastic perturbation into the heat diffusion process. The resultant flow is that of an inhomogeneous heat diffusion perturbed by a stochastic curvature driven motion. The depth in the scene is estimated from the coefficient of the stochastic heat equation without actually knowing the departure from the Gaussian assumption. Further, the proposed method also takes into account the non-convex nature of the diffusion process. The method provides a strong theoretical framework for handling the depth from defocus problem.

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Depth From Defocus: A Real Aperture Imaging Approach

Cited by 183 publications

References 0 publications

Variable focus video: Reconstructing depth and video for dynamic scenes

Variable focus video: Reconstructing depth and video for dynamic scenes

Removing Partial Occlusion from Blurred Thin Occluders

Shape Recovery Using Stochastic Heat Flow

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