Visible-band cameras using silicon imagers
provide excellent video under daylight
conditions, but become blind at night. The
night sky provides illumination from 1-2 μm
which cannot be detected with a silicon sensor.
Adding short-wave infrared detectors to a
CMOS imager would enable a camera which
can be used day or night.
A germanium-enhanced CMOS imager
(TriWave®) has been developed with
broadband sensitivity from 0.4 μm to 1.6 μm.
A 744 x 576 format imager with 10 μm pixel
pitch provides a large field of view without
incurring a size and weight penalty in the
optics. The small pixel size is achieved by
integrating a germanium photodetector into a
mainstream CMOS process. A sensitive
analog signal chain provides a noise floor of 5
electrons. The imagers are hermetically
packaged with a thermo-electric cooler in a
windowed metal package 5 cm<sup>3</sup> in volume. A
compact (<650 cm<sup>3</sup>) camera core has been
designed around the imager. Camera
functions implemented include correlated
double sampling, dark frame subtraction and
In field tests, videos recorded with different
filters in daylight show useful fog and haze
penetration over long distances. Under clear
moonless conditions, short-wave infrared
(SWIR) images recorded with TriWave make
visible individuals that cannot be seen in
videos recorded simultaneously using an
EMCCD. Band-filtered videos confirm that
the night-sky illumination is dominated by
wavelengths above 1200 nm.
Although its lens and image sensor fundamentally limit a digital still camera's imaging performance, image processing
can significantly improve the perceived quality of the output images. A well-designed processing pipeline achieves a
good balance between the available processing power and the image yield (the fraction of images that meet a minimum
This paper describes the use of subjective and objective measurements to establish a methodology for evaluating the
image quality of processing pipelines. The test suite contains images both of analytical test targets for objective
measurements, and of scenes for subjective evaluations that cover the photospace for the intended application.
Objective image quality metrics correlating with perceived sharpness, noise, and color reproduction were used to
evaluate the analytical images. An image quality model estimated the loss in image quality for each metric, and the
individual metrics were combined to estimate the overall image quality. The model was trained with the subjective
image quality data.
The test images were processed through different pipelines, and the overall objective and subjective data was assessed
to identify those image quality metrics that exhibit significant correlation with the perception of image quality. This
methodology offers designers guidelines for effectively optimizing image quality.