  Dark image enhancement aims at converting dark images to normal-light images.
Existing dark image enhancement methods take uncompressed dark images as inputs
and achieve great performance. However, in practice, dark images are often
compressed before storage or transmission over the Internet. Current methods
get poor performance when processing compressed dark images. Artifacts hidden
in the dark regions are amplified by current methods, which results in
uncomfortable visual effects for observers. Based on this observation, this
study aims at enhancing compressed dark images while avoiding compression
artifacts amplification. Since texture details intertwine with compression
artifacts in compressed dark images, detail enhancement and blocking artifacts
suppression contradict each other in image space. Therefore, we handle the task
in latent space. To this end, we propose a novel latent mapping network based
on variational auto-encoder (VAE). Firstly, different from previous VAE-based
methods with single-resolution features only, we exploit multiple latent spaces
with multi-resolution features, to reduce the detail blur and improve image
fidelity. Specifically, we train two multi-level VAEs to project compressed
dark images and normal-light images into their latent spaces respectively.
Secondly, we leverage a latent mapping network to transform features from
compressed dark space to normal-light space. Specifically, since the
degradation models of darkness and compression are different from each other,
the latent mapping process is divided mapping into enlightening branch and
deblocking branch. Comprehensive experiments demonstrate that the proposed
method achieves state-of-the-art performance in compressed dark image
enhancement.
