  Editing flat-looking images into stunning photographs requires skill and
time. Automated image enhancement algorithms have attracted increased interest
by generating high-quality images without user interaction. However, the
quality assessment of a photograph is subjective. Even in tone and color
adjustments, a single photograph of auto-enhancement is challenging to fit user
preferences which are subtle and even changeable. To address this problem, we
present a semiautomatic image enhancement algorithm that can generate
high-quality images with multiple styles by controlling a few parameters. We
first disentangle photo retouching skills from high-quality images and build an
efficient enhancement system for each skill. Specifically, an encoder-decoder
framework encodes the retouching skills into latent codes and decodes them into
the parameters of image signal processing (ISP) functions. The ISP functions
are computationally efficient and consist of only 19 parameters. Despite our
approach requiring multiple inferences to obtain the desired result,
experimental results present that the proposed method achieves state-of-the-art
performances on the benchmark dataset for image quality and model efficiency.
