Unsupervised network for low-light enhancement
Praveen Kandula Maitreya Suin A. N. Rajagopalan
Supervised networks address the task of low-light enhancement using paired
images. However, collecting a wide variety of low-light/clean paired images is
tedious as the scene needs to remain static during imaging. In this paper, we
propose an unsupervised low-light enhancement network using contextguided
illumination-adaptive norm (CIN). Inspired by coarse to fine methods, we
propose to address this task in two stages. In stage-I, a pixel amplifier
module (PAM) is used to generate a coarse estimate with an overall improvement
in visibility and aesthetic quality. Stage-II further enhances the saturated
dark pixels and scene properties of the image using CIN. Different ablation
studies show the importance of PAM and CIN in improving the visible quality of
the image. Next, we propose a region-adaptive single input multiple output
(SIMO) model that can generate multiple enhanced images from a single lowlight
image. The objective of SIMO is to let users choose the image of their liking
from a pool of enhanced images. Human subjective analysis of SIMO results shows
that the distribution of preferred images varies, endorsing the importance of
SIMO-type models. Lastly, we propose a low-light road scene (LLRS) dataset
having an unpaired collection of low-light and clean scenes. Unlike existing
datasets, the clean and low-light scenes in LLRS are real and captured using
fixed camera settings. Exhaustive comparisons on publicly available datasets,
and the proposed dataset reveal that the results of our model outperform prior
art quantitatively and qualitatively.