Time-Constrained Photography

by S. W. Hasinoff, K. N. Kutulakos, F. Durand, and W. T. Freeman

Current designs and evaluations of depth-of-field (DoF) extended cameras all assume a single photo is captured, yet within the same exposure time, a focal stack shot may give rise to a less noisy restoration. Therefore, the authors propose to evaluate the performance of camera designs in the multiple-shots scenario.

The authors firstly develop a Bayesian approach to restore the depth map and the all-in-focus image of a scene; they also estimate the scene-independent restoration error as a function of the interested range of depth and the schedule of capture. Fixing the total exposure time, they seek the optimal number of photos to capture: a few more photos would increase the signal-to-noise ratio (SNR) of the restored image because the dominant source of image noise, the photon noise, is multiplicative; the other source, the additive read noise, would penalize excessive photos though.

According to the expected restoration error, the authors compared the performance of various camera designs. Surprisingly, the conventional camera performs as well as, if not better than, other types of cameras in all but the low time budget settings; it also benefits most from the multiple-shot scheme. In contrast, the coded aperture camera performs poorly in term of light efficiency because its mask blocks most of the light from the sensor.

This paper constitutes a strong defense against intensity masking techniques by alluding the crucial role of time budgets in DoF extension. Indeed, there is no point in designing a DoF extended camera without a time constraint: closing the aperture is most convenient. For the same reason, albeit computational cameras do not outperform conventional ones in most occasions, they serve the most demanding purpose: to capture a sharp image with the minimal exposure.