  Low-light images captured in the real world are inevitably corrupted by
sensor noise. Such noise is spatially variant and highly dependent on the
underlying pixel intensity, deviating from the oversimplified assumptions in
conventional denoising. Existing light enhancement methods either overlook the
important impact of real-world noise during enhancement, or treat noise removal
as a separate pre- or post-processing step. We present \underline{C}oordinated
\underline{E}nhancement for \underline{R}eal-world \underline{L}ow-light Noisy
Images (CERL), that seamlessly integrates light enhancement and noise
suppression parts into a unified and physics-grounded optimization framework.
For the real low-light noise removal part, we customize a self-supervised
denoising model that can easily be adapted without referring to clean
ground-truth images. For the light enhancement part, we also improve the design
of a state-of-the-art backbone. The two parts are then joint formulated into
one principled plug-and-play optimization. Our approach is compared against
state-of-the-art low-light enhancement methods both qualitatively and
quantitatively. Besides standard benchmarks, we further collect and test on a
new realistic low-light mobile photography dataset (RLMP), whose
mobile-captured photos display heavier realistic noise than those taken by
high-quality cameras. CERL consistently produces the most visually pleasing and
artifact-free results across all experiments. Our RLMP dataset and codes are
available at: https://github.com/VITA-Group/CERL.
