While fast imaging in low-light condition is crucial for surveillance and robot applications, it is still a formidable challenge to resolve the seemingly inevitable high noise level and low photon count issues. A variety of image enhancement methods such as de-blurring and de-noising have been proposed in the past. However, limitations can still be found in these methods under extreme low-light condition. To overcome such difficulty, a learning-based image enhancement approach is proposed in this paper. In order to support the development of learning-based methodology, we collected a new low-lighting dataset.
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