  The extremes of lighting (e.g. too much or too little light) usually cause
many troubles for machine and human vision. Many recent works have mainly
focused on under-exposure cases where images are often captured in low-light
conditions (e.g. nighttime) and achieved promising results for enhancing the
quality of images. However, they are inferior to handling images under
over-exposure. To mitigate this limitation, we propose a novel unsupervised
enhancement framework which is robust against various lighting conditions while
does not require any well-exposed images to serve as the ground-truths. Our
main concept is to construct pseudo-ground-truth images synthesized from
multiple source images that simulate all potential exposure scenarios to train
the enhancement network. Our extensive experiments show that the proposed
approach consistently outperforms the current state-of-the-art unsupervised
counterparts in several public datasets in terms of both quantitative metrics
and qualitative results. Our code is available at
https://github.com/VinAIResearch/PSENet-Image-Enhancement.
