Improving the visual quality of the given degraded observation by correcting exposure level
is a fundamental task in the computer vision community. Existing works commonly lack
adaptability towards unknown scenes because of the data-driven patterns (deep networks)
and limited regularization (traditional optimization), and they usually need time-consuming
inference. These two points heavily limit their practicability. In this paper, we establish a
Practical Exposure Corrector (PEC) that assembles the characteristics of efficiency and …