Noise-Optimal Capture for High Dynamic Range Photography

Samuel W. Hasinoff, Frédo Durand, and William T. Freeman


Samuel W. Hasinoff, Frédo Durand, and William T. Freeman, Noise-Optimal Capture for High Dynamic Range Photography. Proc. 23rd IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, pp. 553-560 [pdf]


Taking multiple exposures is a well-established approach both for capturing high dynamic range (HDR) scenes and for noise reduction. But what is the optimal set of photos to capture? The typical approach to HDR capture uses a set of photos with geometrically-spaced exposure times, at a fixed ISO setting (typically ISO 100 or 200). By contrast, we show that the capture sequence with optimal worst-case performance, in general, uses much higher and variable ISO settings, and spends disproportionately longer capturing the dark parts of the scene. Based on a detailed model of noise, we show that optimal capture can be formulated as a mixed integer programming problem. Compared to typical HDR capture, our method lets us achieve higher worst-case SNR in the same capture time (for some cameras, up to 19 dB improvement in the darkest regions), or much faster capture for the same minimum acceptable SNR. Our experiments demonstrate this advantage for both real and synthetic scenes.

Supplementary material

Synthetic example: Nancy church (Figs. 4-6)

Real example: Desk still life (Fig. 7)


This work was supported in part by an NSERC Postdoctoral Fellowship, NSF CAREER award 0447561, the Quanta T-Party, NGA NEGI-1582-04-0004, MURI Grant N00014-06-1-0734, and gifts from Microsoft, Google and Adobe. F. Durand acknowledges a Microsoft Research New Faculty Fellowship and a Sloan Fellowship. Thanks to Rafał Mantiuk for releasing the HDR Nancy church image under a Creative Commons license.