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A variety of denoising, deblurring, and enhancement\ntechniques have been proposed, but their effectiveness is limited in extreme\nconditions, such as video-rate imaging at night. To support the development of\nlearning-based pipelines for low-light image processing, we introduce a dataset\nof raw short-exposure low-light images, with corresponding long-exposure\nreference images. Using the presented dataset, we develop a pipeline for\nprocessing low-light images, based on end-to-end training of a\nfully-convolutional network. The network operates directly on raw sensor data\nand replaces much of the traditional image processing pipeline, which tends to\nperform poorly on such data. We report promising results on the new dataset,\nanalyze factors that affect performance, and highlight opportunities for future\nwork. 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