  Fully- or semi-automatic image enhancement software helps users to increase
the visual appeal of photos and does not require in-depth knowledge of manual
image editing. However, fully-automatic approaches usually enhance the image in
a black-box manner that does not give the user any control over the
optimization process, possibly leading to edited images that do not
subjectively appeal to the user. Semi-automatic methods mostly allow for
controlling which pre-defined editing step is taken, which restricts the users
in their creativity and ability to make detailed adjustments, such as
brightness or contrast. We argue that incorporating user preferences by guiding
an automated enhancement method simplifies image editing and increases the
enhancement's focus on the user. This work thus proposes the Neural Image
Correction & Enhancement Routine (NICER), a neural network based approach to
no-reference image enhancement in a fully-, semi-automatic or fully manual
process that is interactive and user-centered. NICER iteratively adjusts image
editing parameters in order to maximize an aesthetic score based on image style
and content. Users can modify these parameters at any time and guide the
optimization process towards a desired direction. This interactive workflow is
a novelty in the field of human-computer interaction for image enhancement
tasks. In a user study, we show that NICER can improve image aesthetics without
user interaction and that allowing user interaction leads to diverse
enhancement outcomes that are strongly preferred over the unedited image. We
make our code publicly available to facilitate further research in this
direction.
