  We present Curve Distillation, CuDi, for efficient and controllable exposure
adjustment without the requirement of paired or unpaired data during training.
Our method inherits the zero-reference learning and curve-based framework from
an effective low-light image enhancement method, Zero-DCE, with further speed
up in its inference speed, reduction in its model size, and extension to
controllable exposure adjustment. The improved inference speed and lightweight
model are achieved through novel curve distillation that approximates the
time-consuming iterative operation in the conventional curve-based framework by
high-order curve's tangent line. The controllable exposure adjustment is made
possible with a new self-supervised spatial exposure control loss that
constrains the exposure levels of different spatial regions of the output to be
close to the brightness distribution of an exposure map serving as an input
condition. Different from most existing methods that can only correct either
underexposed or overexposed photos, our approach corrects both underexposed and
overexposed photos with a single model. Notably, our approach can additionally
adjust the exposure levels of a photo globally or locally with the guidance of
an input condition exposure map, which can be pre-defined or manually set in
the inference stage. Through extensive experiments, we show that our method is
appealing for its fast, robust, and flexible performance, outperforming
state-of-the-art methods in real scenes. Project page:
https://li-chongyi.github.io/CuDi_files/.
