This paper presents a hybrid technique for measuring conversion gain that blends spatial and temporal information, allowing users to calculate an accurate conversion gain with little knowledge of sensor defects. It blends a single pixel method with multiple pixel methods. We present measured data from a visible CMOS image sensor using two multiple pixel methods and the hybrid method. Additionally, we provide arguments for validity of the hybrid method. To our knowledge, this is the first report of this technique. Conversion gain (e-/DN) directly relates measured digital numbers (DN) to input-referred electrons (e-) for an image sensor. Conversion gain can be directly measured by considering the sensor under varying illumination states in coordination with Poisson statistics. Typically, there are two approaches: measure a single pixel over time or measure a group of pixels at one point in time after correcting for gain non-uniformity. The plotted statistics from these measurements are called either mean-variance or photon-transfer curves. The measurement of a single pixel is relatively straightforward and requires collection of many consecutive frames to get meaningful statistics not dominated by thermal noise. The data volume for an accurate single-pixel measurement can become unwieldy in terms of number of frames required. This is especially true for large format image sensors. In contrast, the measurement of a group of pixels requires fewer consecutive frames, but needs non-uniformity adjustments to correctly calculate statistics.
We present results from a prototype CMOS camera system implementing a multiple sampled pixel level algorithm (“Last
Sample Before Saturation”) in real-time to create High-Dynamic Range (HDR) images that approach the dynamic range
of CCDs. The system is built around a commercial 1280 × 1024 CMOS image sensor with 10-bits per pixel and up to 500
Hz full frame rate with higher frame rates available through windowing. We provide details of system architecture and
present images collected with the system.
KEYWORDS: Signal to noise ratio, High dynamic range imaging, Sensors, Image sensors, Data modeling, Signal detection, CMOS sensors, Field programmable gate arrays, Statistical modeling, Image processing
We present results from a prototype CMOS camera system implementing a multiple sampled pixel level algorithm (“Last Sample Before Saturation”) to create High-Dynamic Range (HDR) images that approach the dynamic range of CCDs. The system is built around a commercial 1280 × 1024 CMOS image sensor with 10-bits per pixel and up to 500 Hz full frame rate with higher frame rates available through windowing. We analyze imagery data collected at room temperature for SNR versus photocurrent, among other figures of merit. Results conform to expectations of a model that uses only dark current, read noise, and photocurrent as input parameters.
In this paper we present the methodology for making absolute quantum efficiency (QE) measurements from the vacuum
ultraviolet (VUV) through the near infrared (NIR) on delta-doped silicon CCDs. Delta-doped detectors provide an
excellent platform to validate measurements through the VUV due to their enhanced UV response. The requirements for
measuring QE through the VUV are more strenuous than measurements in the near UV and necessitate, among other
things, the use of a vacuum monochromator, and good camera vacuum to prevent chip condensation, and more stringent
handling requirements. The system used for these measurements was originally designed for deep UV characterization
of CCDs for the WF/PC instrument on Hubble and later for Cassini CCDs.
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