On the optimal measurement of conversion gain in the presence of dark noise.

Working from a model of Gaussian pixel noise, we present and unify over 25 years of developments in the statistical analysis of the photon transfer conversion gain measurement. We then study a two-sample estimator of the conversion gain that accounts for the general case of non-negligible dark noise. The moments of this estimator are ill-defined (their integral representations diverge), and so we propose a method for assigning pseudomoments, which are shown to agree with actual sample moments under mild conditions.

3D noise photon transfer curve.

A photon transfer curve (PTC) is used to determine fundamental detector noise parameters such as read noise, conversion gain, and fixed pattern noise. Here, the method for determining a PTC is expanded to include 3D noise parameters. 3D noise PTC provides more insight into detector noise and is treated as the next logical step to classical PTC.

Experimentally measuring a detectability index of a computational imaging system.

Computational imaging (CI) systems are an enabling technology for multifunctional cameras capable of performing a wide variety of imaging tasks. However, given the complexity of CI systems, it is often difficult to characterize their performance. In this research, a novel measurement technique is proposed and tested to evaluate the performance of complex non-shift invariant linear CI systems performing a detection task at the system level.

Safety, efficacy and repeatability of a novel house dust mite allergen challenge technique in the Fraunhofer allergen challenge chamber.

Background: Efficacy testing of immunotherapy in field studies is often hampered by variation of airborne allergens. Standardized allergen exposure in challenge chamber settings might be an alternative. Therefore, we developed a universal technique to create an atmosphere loaded with allergen particles of adjustable size from aqueous solutions of licensed allergen extracts.

Finite sampling corrected 3D noise with confidence intervals.

When evaluated with a spatially uniform irradiance, an imaging sensor exhibits both spatial and temporal variations, which can be described as a three-dimensional (3D) random process considered as noise. In the 1990s, NVESD engineers developed an approximation to the 3D power spectral density for noise in imaging systems known as 3D noise. The goal was to decompose the 3D noise process into spatial and temporal components identify potential sources of origin.