  Low-light image enhancement is an inherently subjective process whose targets
vary with the user's aesthetic. Motivated by this, several personalized
enhancement methods have been investigated. However, the enhancement process
based on user preferences in these techniques is invisible, i.e., a "black
box". In this work, we propose an intelligible unsupervised personalized
enhancer (iUPEnhancer) for low-light images, which establishes the correlations
between the low-light and the unpaired reference images with regard to three
user-friendly attributions (brightness, chromaticity, and noise). The proposed
iUP-Enhancer is trained with the guidance of these correlations and the
corresponding unsupervised loss functions. Rather than a "black box" process,
our iUP-Enhancer presents an intelligible enhancement process with the above
attributions. Extensive experiments demonstrate that the proposed algorithm
produces competitive qualitative and quantitative results while maintaining
excellent flexibility and scalability. This can be validated by personalization
with single/multiple references, cross-attribution references, or merely
adjusting parameters.
