  The pursuit of smaller pixel sizes at ever increasing resolution in digital
image sensors is mainly driven by the stringent price and form-factor
requirements of sensors and optics in the cellular phone market. Recently, Eric
Fossum proposed a novel concept of an image sensor with dense sub-diffraction
limit one-bit pixels jots, which can be considered a digital emulation of
silver halide photographic film. This idea has been recently embodied as the
EPFL Gigavision camera. A major bottleneck in the design of such sensors is the
image reconstruction process, producing a continuous high dynamic range image
from oversampled binary measurements. The extreme quantization of the Poisson
statistics is incompatible with the assumptions of most standard image
processing and enhancement frameworks. The recently proposed maximum-likelihood
(ML) approach addresses this difficulty, but suffers from image artifacts and
has impractically high computational complexity. In this work, we study a
variant of a sensor with binary threshold pixels and propose a reconstruction
algorithm combining an ML data fitting term with a sparse synthesis prior. We
also show an efficient hardware-friendly real-time approximation of this
inverse operator.Promising results are shown on synthetic data as well as on
HDR data emulated using multiple exposures of a regular CMOS sensor.
