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Many flagship smartphone cameras now use a dedicated neural image signalprocessor (ISP) to render noisy raw sensor images to the final processedoutput. Training nightmode ISP networks relies on large-scale datasets of imagepairs with: (1) a noisy raw image captured with a short exposure and a high ISOgain; and (2) a ground truth low-noise raw image captured with a long exposureand low ISO that has been rendered through the ISP. Capturing such image pairsis tedious and time-consuming, requiring careful setup to ensure alignmentbetween the image pairs. In addition, ground truth images are often prone tomotion blur due to the long exposure. To address this problem, we propose amethod that synthesizes nighttime images from daytime images. Daytime imagesare easy to capture, exhibit low-noise (even on smartphone cameras) and rarelysuffer from motion blur. We outline a processing framework to convert daytimeraw images to have the appearance of realistic nighttime raw images withdifferent levels of noise. Our procedure allows us to easily produce alignednoisy and clean nighttime image pairs. We show the effectiveness of oursynthesis framework by training neural ISPs for nightmode rendering.Furthermore, we demonstrate that using our synthetic nighttime images togetherwith small amounts of real data (e.g., 5% to 10%) yields performance almost onpar with training exclusively on real nighttime images. Our dataset and codeare available at https://github.com/SamsungLabs/day-to-night.

Day-to-Night Image Synthesis for Training Nighttime Neural ISPs