Light-Efficient Photography

Hasinoff, Samuel W.; Kutulakos, Kiriakos N.

doi:10.1007/978-3-540-88693-8_4

Cited by 17 publications

(10 citation statements)

References 22 publications

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“…A focal stack, a set of images focused at different depths, can be combined to simulate extended depth of field [Hasinoff and Kutulakos 2008]; reduced depth of field can be obtained likewise [Jacobs et al 2012]. If the user can interactively specify the desired manipulation prior to capture and verify it via visualization in the viewfinder, the autofocus routine can deduce the minimal focal stack needed.…”

Section: Stack Focusing For Displaymentioning

confidence: 99%

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WYSIWYG computational photography via viewfinder editing

et al. 2013

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Figure 1: Viewfinder editing and appearance-based metering. (a) In the proposed system, the user interacts with the camera viewfinder as if it were a live canvas, by making sparse strokes to select regions and to specify edits. The edits propagate spatiotemporally as the camera and the scene objects move, and the edits are applied in the subsequent frames of the viewfinder, which are tonemapped HDR images, to provide WYSIWYG experience. (b) After making a local tonal edit, the user presses the shutter to trigger a high-resolution capture. Here we show the resulting tonemapped HDR composite without any edits applied, for reference. The edit mask computed from the user strokes is shown in the inset. (c) The result with edits applied is shown. This approximately corresponds to what the user sees on the screen just as he presses the shutter. Our appearance-based metering acquired an exposure stack at (0.645 ms, 5.555 ms, 11.101 ms) by optimizing the quality of the final result based on the user's global and local edits. (d) The regions indicated in (c), magnified. (e) We captured another stack with a histogram-based HDR metering at (0.579 ms, 9.958 ms, 23.879 ms) and applied the same post-processing pipeline. As the standard HDR metering considers equally all the pixels in the scene, it allocates too much effort to capture the dark areas that were not as important to the user, leading to a longer capture times that invite ghosting (top) and higher noise in mid-tones (bottom). AbstractDigital cameras with electronic viewfinders provide a relatively faithful depiction of the final image, providing a WYSIWYG experience. If, however, the image is created from a burst of differently captured images, or non-linear interactive edits significantly alter the final outcome, then the photographer cannot directly see the results, but instead must imagine the post-processing effects. This paper explores the notion of viewfinder editing, which makes the viewfinder more accurately reflect the final image the user intends to create. We allow the user to alter the local or global appearance (tone, color, saturation, or focus) via stroke-based input, and propagate the edits spatiotemporally. The system then delivers a real-time visualization of these modifications to the user, and drives the camera control routines to select better capture parameters.

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Section: Stack Focusing For Displaymentioning

confidence: 99%

“…Focus bracketing can be used to digitally extend the depth of field [Hasinoff and Kutulakos 2008] or reduce it [Jacobs et al 2012]. These algorithms register and blend images taken at various focus settings to mimic a photograph with a different aperture setting.…”

Section: Optimizing Capture Parametersmentioning

confidence: 99%

WYSIWYG computational photography via viewfinder editing

et al. 2013

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“…Given the focal length, diameter of the aperture, and focus setting, the defocus blur only depends on the depth of the scenes. The diameter of the blur kernel at distance d can be computed from the geometry of optical system [16,8], as shown in Figure 2. It is represented as…”

Section: Relative Blur Versus Disparity (Rbd) Modelmentioning

confidence: 99%

Blur-Aware Disparity Estimation from Defocus Stereo Images

Chen

Zhong

Ahonen

2015

2015 IEEE International Conference on Computer Vision (ICCV)

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Defocus blur usually causes performance degradation in establishing the visual correspondence between stereo images. We propose a blur-aware disparity estimation method that is robust to the mismatch of focus in stereo images. The relative blur resulting from the mismatch of focus between stereo images is approximated as the difference of the square diameters of the blur kernels. Based on the defocus and stereo model, we propose the relative blur versus disparity (RBD) model that characterizes the relative blur as a second-order polynomial function of disparity. Our method alternates between RBD model update and disparity update in each iteration. The RBD model in return refines the disparity estimation by updating the matching cost and aggregation weight to compensate the mismatch of focus. Experiments using both synthesized and real datasets demonstrate the effectiveness of our proposed algorithm.

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“…Multi-image based method (e.g. [9,10,11]) has been widely studied in the past years. Recently, the more challenging single image based work is presented.…”

Section: Previous Workmentioning

confidence: 99%

“…The maximization problem in (13) can also be rendered as the minimization of an energy term defined using Eq (9) and Eq (13)- (17).…”

Section: Recovery Of the Focused Sharp Imagementioning

confidence: 99%

Single image focus editing

Zhang

Cham

2009

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops

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In this paper, we present a postprocessing approach to tackle the single image focus editing problem. In detail, the proposed method can accomplish the tasks of focus map estimation, image refocusing and defocusing. Given an image with a mixture of focused and defocused objects, we first detect the edges and then estimate the focus map based on the edge blurriness which is depicted explicitly with a well-parameterized model. The image refocusing problem is addressed in an elaborate blind deconvolution framework, where the image prior is modeled well by using both global and local constraints. Especially, we correct the defocused blurry edges to sharp ones with the aid of the parametric edge model and then render this cue as a novel local prior to ensure the sharpness of the refocused image. Experimental results demonstrate that the proposed approach performs well in producing different styles of realistic images from a single input by focus editing.

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Light-Efficient Photography

Abstract: Capturing multiple photos at different focus

Cited by 17 publications

References 22 publications

WYSIWYG computational photography via viewfinder editing

WYSIWYG computational photography via viewfinder editing

Blur-Aware Disparity Estimation from Defocus Stereo Images

Single image focus editing

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