  Low-light imaging on mobile devices is typically challenging due to
insufficient incident light coming through the relatively small aperture,
resulting in a low signal-to-noise ratio. Most of the previous works on
low-light image processing focus either only on a single task such as
illumination adjustment, color enhancement, or noise removal; or on a joint
illumination adjustment and denoising task that heavily relies on short-long
exposure image pairs collected from specific camera models, and thus these
approaches are less practical and generalizable in real-world settings where
camera-specific joint enhancement and restoration is required. To tackle this
problem, in this paper, we propose a low-light image processing framework that
performs joint illumination adjustment, color enhancement, and denoising.
Considering the difficulty in model-specific data collection and the ultra-high
definition of the captured images, we design two branches: a coefficient
estimation branch as well as a joint enhancement and denoising branch. The
coefficient estimation branch works in a low-resolution space and predicts the
coefficients for enhancement via bilateral learning, whereas the joint
enhancement and denoising branch works in a full-resolution space and
progressively performs joint enhancement and denoising. In contrast to existing
methods, our framework does not need to recollect massive data when being
adapted to another camera model, which significantly reduces the efforts
required to fine-tune our approach for practical usage. Through extensive
experiments, we demonstrate its great potential in real-world low-light imaging
applications when compared with current state-of-the-art methods.
