  There is an increasing consensus that the design and optimization of low
light image enhancement methods need to be fully driven by perceptual quality.
With numerous approaches proposed to enhance low-light images, much less work
has been dedicated to quality assessment and quality optimization of low-light
enhancement. In this paper, to close the gap between enhancement and
assessment, we propose a loop enhancement framework that produces a clear
picture of how the enhancement of low-light images could be optimized towards
better visual quality. In particular, we create a large-scale database for
QUality assessment Of The Enhanced LOw-Light Image (QUOTE-LOL), which serves as
the foundation in studying and developing objective quality assessment
measures. The objective quality assessment measure plays a critical bridging
role between visual quality and enhancement and is further incorporated in the
optimization in learning the enhancement model towards perceptual optimally.
Finally, we iteratively perform the enhancement and optimization tasks,
enhancing the low-light images continuously. The superiority of the proposed
scheme is validated based on various low-light scenes. The database as well as
the code will be available.
