A Layer-Based Restoration Framework for Variable-Aperture Photography

Hasinoff, Samuel W.; Kutulakos, Kiriakos N.

doi:10.1109/iccv.2007.4408898

Cited by 58 publications

(42 citation statements)

References 26 publications

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“…To obtain depth this way, traditional DFD approaches capture multiple images of the same scene with varying focal distance (e.g., [5]) or aperture size [6]. When the camera is calibrated, this is often sufficient to disambiguate between depth and surface radiance.…”

Section: Related Workmentioning

confidence: 99%

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Depth and Deblurring from a Spectrally-Varying Depth-of-Field

Chakrabarti

Zickler

2012

Computer Vision – ECCV 2012

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Abstract. We propose modifying the aperture of a conventional color camera so that the effective aperture size for one color channel is smaller than that for the other two. This produces an image where different color channels have different depths-of-field, and from this we can computationally recover scene depth, reconstruct an all-focus image and achieve synthetic re-focusing, all from a single shot. These capabilities are enabled by a spatio-spectral image model that encodes the statistical relationship between gradient profiles across color channels. This approach substantially improves depth accuracy over alternative single-shot codedaperture designs, and since it avoids introducing additional spatial distortions and is light efficient, it allows high-quality deblurring and lower exposure times. We demonstrate these benefits with comparisons on synthetic data, as well as results on images captured with a prototype lens.

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Section: Related Workmentioning

confidence: 99%

“…where L is the likelihood measure defined in (6). In addition to choosing the most likely depth, we also seek to characterize the confidence of this choice.…”

Section: Local Inferencementioning

confidence: 99%

Depth and Deblurring from a Spectrally-Varying Depth-of-Field

Chakrabarti

Zickler

2012

Computer Vision – ECCV 2012

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“…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. Like our method, others have recently addressed background restoration near blurred edges: Bhasin and Chaudhuri [2], Favaro and Soatto [6], and Hasinoff and Kutulakos [7]. While each of these methods can perform background estimation, they do so as part of a full scene reconstruction that includes its 3D shape.…”

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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“…In addition to varying exposure, varying the ISO parameters can further enhance performance of HDR imaging in noisy and low light areas [Hasinoff et al 2010]. There have also been some attempts to acquire HDR images by varying the aperture in a camera [Hasinoff and Kutulakos 2007], but in such cases the varying depth of field in the images needs to be taken into account.…”

Section: Hdr Imagingmentioning

confidence: 99%

“…Hasinoff and Kutulakos [Hasinoff and Kutulakos 2007] showed that acquiring images with varying aperture sizes enables interesting and useful rendering effects, including depth of field manipulation and HDR acquisition. Our compressive reconstruction algorithm enable a significant reduction in the number of aperture images that need to be acquired by effectively interpolating images with unseen apertures.…”

Section: Hdr Imagingmentioning

confidence: 99%

Compressive epsilon photography for post-capture control in digital imaging

et al. 2014

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A traditional camera requires the photographer to select the many parameters at capture time. While advances in light field photography have enabled post-capture control of focus and perspective, they suffer from several limitations including lower spatial resolution, need for hardware modifications, and restrictive choice of aperture and focus setting. In this paper, we propose "compressive epsilon photography," a technique for achieving complete postcapture control of focus and aperture in a traditional camera by acquiring a carefully selected set of 8 to 16 images and computationally reconstructing images corresponding to all other focus-aperture settings. We make the following contributions: first, we learn the statistical redundancies in focal-aperture stacks using a Gaussian Mixture Model; second, we derive a greedy sampling strategy for selecting the best focus-aperture settings; and third, we develop an algorithm for reconstructing the entire focal-aperture stack from a few captured images. As a consequence, only a burst of images with carefully selected camera settings are acquired. Post-capture, the user can then select any focal-aperture setting of choice and the corresponding image can be rendered using our algorithm. We show extensive results on several real data sets.

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A Layer-Based Restoration Framework for Variable-Aperture Photography

Cited by 58 publications

References 26 publications

Depth and Deblurring from a Spectrally-Varying Depth-of-Field

Depth and Deblurring from a Spectrally-Varying Depth-of-Field

Removing Partial Occlusion from Blurred Thin Occluders

Compressive epsilon photography for post-capture control in digital imaging

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