  Many flagship smartphone cameras now use a dedicated neural image signal
processor (ISP) to render noisy raw sensor images to the final processed
output. Training nightmode ISP networks relies on large-scale datasets of image
pairs with: (1) a noisy raw image captured with a short exposure and a high ISO
gain; and (2) a ground truth low-noise raw image captured with a long exposure
and low ISO that has been rendered through the ISP. Capturing such image pairs
is tedious and time-consuming, requiring careful setup to ensure alignment
between the image pairs. In addition, ground truth images are often prone to
motion blur due to the long exposure. To address this problem, we propose a
method that synthesizes nighttime images from daytime images. Daytime images
are easy to capture, exhibit low-noise (even on smartphone cameras) and rarely
suffer from motion blur. We outline a processing framework to convert daytime
raw images to have the appearance of realistic nighttime raw images with
different levels of noise. Our procedure allows us to easily produce aligned
noisy and clean nighttime image pairs. We show the effectiveness of our
synthesis framework by training neural ISPs for nightmode rendering.
Furthermore, we demonstrate that using our synthetic nighttime images together
with small amounts of real data (e.g., 5% to 10%) yields performance almost on
par with training exclusively on real nighttime images. Our dataset and code
are available at https://github.com/SamsungLabs/day-to-night.
