Light-Efficient Photography

Samuel W. Hasinoff and Kiriakos N. Kutulakos

Publications

Samuel W. Hasinoff and Kiriakos N. Kutulakos, Light-Efficient Photography. IEEE Trans. Pattern Analysis and Machine Intelligence, 33(11), pp. 2203-2214, 2011. [pdf]

Samuel W. Hasinoff and Kiriakos N. Kutulakos, Light-Efficient Photography. Proc. 10th European Conference on Computer Vision, ECCV 2008, pp. 45-59. [pdf]

Kiriakos N. Kutulakos and Samuel W. Hasinoff, Focal Stack Photography: High-Performance Photography with a Conventional Camera. Proc. 11th IAPR Conference on Machine Vision Applications, MVA 2009, pp. 332-337 (invited paper). [pdf]

Samuel W. Hasinoff, Variable-Aperture Photography. PhD Thesis, University of Toronto, Dept. of Computer Science, 2008. [pdf]
Alain Fournier Ph.D. Thesis Award

Journal abstract

In this article we consider the problem of imaging a scene with a given depth of field at a given exposure level in the shortest amount of time possible. We show that by (1) collecting a sequence of photos and (2) controlling the aperture, focus and exposure time of each photo individually, we can span the given depth of field in less total time than it takes to expose a single narrower-aperture photo. Using this as a starting point, we obtain two key results. First, for lenses with continuously-variable apertures, we derive a closed-form solution for the globally optimal capture sequence, i.e., that collects light from the specified depth of field in the most efficient way possible. Second, for lenses with discrete apertures, we derive an integer programming problem whose solution is the optimal sequence. Our results are applicable to off-the-shelf cameras and typical photography conditions, and advocate the use of dense, wide-aperture photo sequences as a light-efficient alternative to single-shot, narrow-aperture photography.

Supplementary material

Proofs omitted from the conference paper [pdf]
Detailed description of the experimental setup, results, and videos [pdf]
Overview video of the results (videos below are higher-res) [divx avi]
PowerPoint slides, presented at ECCV 2008 [zip]

"hamster" dataset

ground truth - narrow-aperture image (f16), defining the DOF
input images - sample wide-aperture input image (f1.2), 3 of 14
results - synthetic DOF composite, created according to a coarse depth map
video - varying the aperture from f16 to f1.2, with fixed focus setting (brass figurine) [downsampled] [full-res crop]

video - sweeping the focus setting over the DOF, with an aperture of f2.8 [downsampled] [full-res crop]

"face" dataset

ground truth - narrow-aperture image (f16), defining the DOF
input images - sample wide-aperture input image (f1.2), 6 of 14
results - synthetic DOF composite, created according to a coarse depth map
video - varying the aperture from f16 to f1.2, with fixed focus setting (right eye) [downsampled]

video - sweeping the focus setting over the DOF, with an aperture of f2.8 [downsampled]

"simpsons" dataset

ground truth - narrow-aperture image (f8), defining the DOF
input images - sample wide-aperture input image (f2.7), 1 of 4
results - synthetic DOF composite, created according to a coarse depth map
video - varying the aperture from f8 to f2.7, with fixed focus setting (front tea canister) [downsampled]

video - sweeping the focus setting over the DOF, with an aperture of f3.2 [downsampled]

Acknowledgements

This work was supported in part by the Natural Sciences and Engineering Research Council of Canada under the RGPIN and EQPEQ programs, and by an Ontario Premier's Research Excellence Award.