  Imaging under photon-scarce situations introduces challenges to many
applications as the captured images are with low signal-to-noise ratio and poor
luminance. In this paper, we investigate the raw image restoration under
low-photon-count conditions by simulating the imaging of quanta image sensor
(QIS). We develop a lightweight framework, which consists of a multi-level
pyramid denoising network (MPDNet) and a luminance adjustment (LA) module to
achieve separate denoising and luminance enhancement. The main component of our
framework is the multi-skip attention residual block (MARB), which integrates
multi-scale feature fusion and attention mechanism for better feature
representation. Our MPDNet adopts the idea of Laplacian pyramid to learn the
small-scale noise map and larger-scale high-frequency details at different
levels, and feature extractions are conducted on the multi-scale input images
to encode richer contextual information. Our LA module enhances the luminance
of the denoised image by estimating its illumination, which can better avoid
color distortion. Extensive experimental results have demonstrated that our
image restorer can achieve superior performance on the degraded images with
various photon levels by suppressing noise and recovering luminance and color
effectively.
