8,455 research outputs found
Rank-based camera spectral sensitivity estimation
In order to accurately predict a digital camera response to spectral stimuli, the spectral sensitivity functions of its sensor need to be known. These functions can be determined by direct measurement in the lab—a difficult and lengthy procedure—or through simple statistical inference. Statistical inference methods are based on the observation that when a camera responds linearly to spectral stimuli, the device spectral sensitivities are linearly related to the camera rgb response values, and so can be found through regression. However, for rendered images, such as the JPEG images taken by a mobile phone, this assumption of linearity is violated. Even small departures from linearity can negatively impact the accuracy of the recovered spectral sensitivities, when a regression method is used. In our work, we develop a novel camera spectral sensitivity estimation technique that can recover the linear device spectral sensitivities from linear images and the effective linear sensitivities from rendered images. According to our method, the rank order of a pair of responses imposes a constraint on the shape of the underlying spectral sensitivity curve (of the sensor). Technically, each rank-pair splits the space where the underlying sensor might lie in two parts (a feasible region and an infeasible region). By intersecting the feasible regions from all the ranked-pairs, we can find a feasible region of sensor space. Experiments demonstrate that using rank orders delivers equal estimation to the prior art. However, the Rank-based method delivers a step-change in estimation performance when the data is not linear and, for the first time, allows for the estimation of the effective sensitivities of devices that may not even have “raw mode.” Experiments validate our method
Performance of the Gemini Planet Imager Non-Redundant Mask and spectroscopy of two close-separation binaries HR 2690 and HD 142527
The Gemini Planet Imager (GPI) contains a 10-hole non-redundant mask (NRM),
enabling interferometric resolution in complement to its coronagraphic
capabilities. The NRM operates both in spectroscopic (integral field
spectrograph, henceforth IFS) and polarimetric configurations. NRM observations
were taken between 2013 and 2016 to characterize its performance. Most
observations were taken in spectroscopic mode with the goal of obtaining
precise astrometry and spectroscopy of faint companions to bright stars. We
find a clear correlation between residual wavefront error measured by the AO
system and the contrast sensitivity by comparing phase errors in observations
of the same source, taken on different dates. We find a typical 5-
contrast sensitivity of at . We explore the
accuracy of spectral extraction of secondary components of binary systems by
recovering the signal from a simulated source injected into several datasets.
We outline data reduction procedures unique to GPI's IFS and describe a newly
public data pipeline used for the presented analyses. We demonstrate recovery
of astrometry and spectroscopy of two known companions to HR 2690 and HD
142527. NRM+polarimetry observations achieve differential visibility precision
of in the best case. We discuss its limitations on
Gemini-S/GPI for resolving inner regions of protoplanetary disks and prospects
for future upgrades. We summarize lessons learned in observing with NRM in
spectroscopic and polarimetric modes.Comment: Accepted to AJ, 22 pages, 14 figure
- …