Low-light Image Enhancement via Breaking Down the Darkness
- URL: http://arxiv.org/abs/2111.15557v1
- Date: Tue, 30 Nov 2021 16:50:59 GMT
- Title: Low-light Image Enhancement via Breaking Down the Darkness
- Authors: Qiming Hu, Xiaojie Guo
- Abstract summary: This paper presents a novel framework inspired by the divide-and-rule principle.
We propose to convert an image from the RGB space into a luminance-chrominance one.
An adjustable noise suppression network is designed to eliminate noise in the brightened luminance.
The enhanced luminance further serves as guidance for the chrominance mapper to generate realistic colors.
- Score: 8.707025631892202
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Images captured in low-light environment often suffer from complex
degradation. Simply adjusting light would inevitably result in burst of hidden
noise and color distortion. To seek results with satisfied lighting,
cleanliness, and realism from degraded inputs, this paper presents a novel
framework inspired by the divide-and-rule principle, greatly alleviating the
degradation entanglement. Assuming that an image can be decomposed into texture
(with possible noise) and color components, one can specifically execute noise
removal and color correction along with light adjustment. Towards this purpose,
we propose to convert an image from the RGB space into a luminance-chrominance
one. An adjustable noise suppression network is designed to eliminate noise in
the brightened luminance, having the illumination map estimated to indicate
noise boosting levels. The enhanced luminance further serves as guidance for
the chrominance mapper to generate realistic colors. Extensive experiments are
conducted to reveal the effectiveness of our design, and demonstrate its
superiority over state-of-the-art alternatives both quantitatively and
qualitatively on several benchmark datasets. Our code is publicly available at
https://github.com/mingcv/Bread.
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