KinD-LCE Curve Estimation And Retinex Fusion On Low-Light Image
- URL: http://arxiv.org/abs/2207.09210v3
- Date: Mon, 23 Oct 2023 05:58:45 GMT
- Title: KinD-LCE Curve Estimation And Retinex Fusion On Low-Light Image
- Authors: Xiaochun Lei, Weiliang Mai, Junlin Xie, He Liu, Zetao Jiang, Zhaoting
Gong, Chang Lu, Linjun Lu
- Abstract summary: This paper proposes an algorithm for low illumination enhancement.
KinD-LCE uses a light curve estimation module to enhance the illumination map in the Retinex decomposed image.
An illumination map and reflection map fusion module were also proposed to restore the image details and reduce detail loss.
- Score: 7.280719886684936
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Low-light images often suffer from noise and color distortion. Object
detection, semantic segmentation, instance segmentation, and other tasks are
challenging when working with low-light images because of image noise and
chromatic aberration. We also found that the conventional Retinex theory loses
information in adjusting the image for low-light tasks. In response to the
aforementioned problem, this paper proposes an algorithm for low illumination
enhancement. The proposed method, KinD-LCE, uses a light curve estimation
module to enhance the illumination map in the Retinex decomposed image,
improving the overall image brightness. An illumination map and reflection map
fusion module were also proposed to restore the image details and reduce detail
loss. Additionally, a TV(total variation) loss function was applied to
eliminate noise. Our method was trained on the GladNet dataset, known for its
diverse collection of low-light images, tested against the Low-Light dataset,
and evaluated using the ExDark dataset for downstream tasks, demonstrating
competitive performance with a PSNR of 19.7216 and SSIM of 0.8213.
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