Abstract
Low light short exposure photography is challenging, but an important factor in capturing images in temporarily dynamic scenes avoiding unwanted effects such as ghosting, motion blur, camera shakes, image artifacts, etc. Monochrome augmented low-light image enhancement aims to get improved low-light short-exposure images by using an additional monochrome sensor and its data. Monochrome images typically possess a higher SNR (Signal-to-Noise Ratio) and better luma information, since it avoids the attenuation by the Bayer Filter. The objective here is to develop a deep learning based approach to enhance low-light short exposure images from the main sensor by using an additional low resolution monochrome sensor.
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