Latent Disentanglement for Low Light Image Enhancement

Mooi Choo Chuah, Zhihao Zheng

Abstract:

Many learning-based low-light image enhancement (LLIE) algorithms are based on the Retinex theory.
However, the Retinex-based decomposition techniques in such
models introduce corruptions which limit their enhancement
performance. In this paper, we propose a Latent Disentanglebased Enhancement Network (LDE-Net) for low light vision
tasks. The latent disentanglement module disentangles the
input image in latent space such that no corruption remains
in the disentangled Content and Illumination components. For
LLIE task, we design a Content-Aware Embedding (CAE)
module that utilizes Content features to direct the enhancement of the Illumination component. For downstream tasks
(e.g. nighttime UAV tracking and low-light object detection),
we develop an effective light-weight enhancer based on the latent disentanglement framework. Comprehensive quantitative
and qualitative experiments demonstrate that our LDE-Net
significantly outperforms state-of-the-art methods on various
LLIE benchmarks. In addition, the great results obtained
by applying our framework on the downstream tasks also
demonstrate the usefulness of our latent disentanglement
design.