Zero-shot Learning for Low-light Image Enhancement Based on Dual Iteration

XIANG Sen; WANG Yingfeng; DENG Huiping; WU Jin; YU Li

doi:10.11999/JEIT211593

Volume 44 Issue 10

Oct. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(10): 3379-3388

XIANG Sen, WANG Yingfeng, DENG Huiping, WU Jin, YU Li. Zero-shot Learning for Low-light Image Enhancement Based on Dual Iteration[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3379-3388. doi: 10.11999/JEIT211593

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XIANG Sen, WANG Yingfeng, DENG Huiping, WU Jin, YU Li. Zero-shot Learning for Low-light Image Enhancement Based on Dual Iteration[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3379-3388. doi: 10.11999/JEIT211593

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Zero-shot Learning for Low-light Image Enhancement Based on Dual Iteration

doi: 10.11999/JEIT211593

1.
School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430080, China
2.
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China

Funds: The National Natural Science Foundation of China (61702384, 62001180, 61871437)

Received Date: 2021-12-29
Rev Recd Date: 2022-03-19

Available Online: 2022-04-17

Publish Date: 2022-10-19

Abstract

Abstract

In this paper, a novel zero-shot low-light image enhancement framework is proposed based on dual iterations. The outer iteration uses a network to estimate the enhancement parameters, with which the inner iteration improves actually the image, and the results are applied to calculating the loss functions and updating the outer network. After multiple rounds of iterations, high-quality images can be obtained. Within this framework, an adaptive parameter estimation module and an attention-based pixel-wise atmosphere estimation module are designed. In addition, unsupervised loss functions based on light, contrast, color balance and image smoothness priors are proposed. Experiments demonstrate that the proposed method obtains high quality clear images from low-light ones, and outperforms state-of-the-art methods. Furthermore, the proposed method belongs to zero-shot learning, which does not need training dataset and thus can be widely applied.
- Image enhancement,
- Low-light,
- Unsupervised learning,
- Zero-shot learning,
- Iterative enhancement

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References(27)

References

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