Unpaired Night-To-Day Translation: Image Restoration And Style Transfer Under Low Illumination

The faint vision at low illumination affects the performance of intelligent surveillance systems and induces criminals to sin under the cover of darkness. Night-to-day translation is an ideal way to handle this problem, but hard to achieve due to the lack of information at night. We propose a novel approach that combines DCGAN(deep convolutional generative adversarial network) and image processing algorithms to find out the mapping from night to day. Images from night domain are enhanced with MSRCP(multi-scale retinex with chromaticity preservation) algorithm before they're put into DCGAN to generate bright and clear daylight images without paired supervision. At the same time emerging issues of image atomization and local over-exposure are handled to ensure the quality of output. The experimental results show that our approach can be applied to different conditions and dig out sensitive information from the darkness.