CEL-Net: Continuous Exposure for Extreme Low-Light Imaging
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Deep learning methods for enhancing dark images learn a mapping from input images to output images with pre-determined discrete exposure levels. Often, at inference time the input and optimal output exposure levels of the given image are different from the seen ones during training. As a result the enhanced image might suffer from visual distortions, such as low contrast or dark areas. We address this issue by introducing a deep learning model that can continuously generalize at inference time to unseen exposure levels without the need to retrain the model. To this end, we introduce a dataset of 1500 raw images captured in both outdoor and indoor scenes, with five different exposure levels and various camera parameters. Using the dataset, we develop a model for extreme low-light imaging that can continuously tune the input or output exposure level of the image to an unseen one. We investigate the properties of our model and validate its performance, showing promising results.
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