  Super-resolution tasks oriented to images captured in ultra-dark environments
is a practical yet challenging problem that has received little attention. Due
to uneven illumination and low signal-to-noise ratio in dark environments, a
multitude of problems such as lack of detail and color distortion may be
magnified in the super-resolution process compared to normal-lighting
environments. Consequently, conventional low-light enhancement or
super-resolution methods, whether applied individually or in a cascaded manner
for such problem, often encounter limitations in recovering luminance, color
fidelity, and intricate details. To conquer these issues, this paper proposes a
specialized dual-modulated learning framework that, for the first time,
attempts to deeply dissect the nature of the low-light super-resolution task.
Leveraging natural image color characteristics, we introduce a self-regularized
luminance constraint as a prior for addressing uneven lighting. Expanding on
this, we develop Illuminance-Semantic Dual Modulation (ISDM) components to
enhance feature-level preservation of illumination and color details. Besides,
instead of deploying naive up-sampling strategies, we design the
Resolution-Sensitive Merging Up-sampler (RSMU) module that brings together
different sampling modalities as substrates, effectively mitigating the
presence of artifacts and halos. Comprehensive experiments showcases the
applicability and generalizability of our approach to diverse and challenging
ultra-low-light conditions, outperforming state-of-the-art methods with a
notable improvement (i.e., $\uparrow$5\% in PSNR, and $\uparrow$43\% in LPIPS).
Especially noteworthy is the 19-fold increase in the RMSE score, underscoring
our method's exceptional generalization across different darkness levels. The
code will be available online upon publication of the paper.
