  Taking photographs in low light using a mobile phone is challenging and
rarely produces pleasing results. Aside from the physical limits imposed by
read noise and photon shot noise, these cameras are typically handheld, have
small apertures and sensors, use mass-produced analog electronics that cannot
easily be cooled, and are commonly used to photograph subjects that move, like
children and pets. In this paper we describe a system for capturing clean,
sharp, colorful photographs in light as low as 0.3~lux, where human vision
becomes monochromatic and indistinct. To permit handheld photography without
flash illumination, we capture, align, and combine multiple frames. Our system
employs "motion metering", which uses an estimate of motion magnitudes (whether
due to handshake or moving objects) to identify the number of frames and the
per-frame exposure times that together minimize both noise and motion blur in a
captured burst. We combine these frames using robust alignment and merging
techniques that are specialized for high-noise imagery. To ensure accurate
colors in such low light, we employ a learning-based auto white balancing
algorithm. To prevent the photographs from looking like they were shot in
daylight, we use tone mapping techniques inspired by illusionistic painting:
increasing contrast, crushing shadows to black, and surrounding the scene with
darkness. All of these processes are performed using the limited computational
resources of a mobile device. Our system can be used by novice photographers to
produce shareable pictures in a few seconds based on a single shutter press,
even in environments so dim that humans cannot see clearly.
