基于双重迭代的零样本低照度图像增强

向森; 王应锋; 邓慧萍; 吴谨; 喻莉

doi:10.11999/JEIT211593

基于双重迭代的零样本低照度图像增强

doi: 10.11999/JEIT211593

向森^1, ,,
王应锋¹,
邓慧萍¹,
吴谨¹,
喻莉²

1.
武汉科技大学信息科学与工程学院武汉 430080
2.
华中科技大学电子信息与通信学院武汉 430074

基金项目: 国家自然科学基金(61702384, 62001180, 61871437)

详细信息

作者简介:
向森：男，副教授，研究方向为图像处理、机器学习

王应锋：男，硕士生，研究方向为图像处理

邓慧萍：女，副教授，研究方向为图像处理、机器学习

吴谨：女，教授，研究方向为图像处理、机器学习

喻莉：女，教授，研究方向为多媒体通信

通讯作者:
向森　xiangsen@wust.edu.cn

¹⁾ 夜间实拍图像测试集网址: https://github.com/Feng-wy/Zero-shot-dual-iter-LLE
中图分类号: TN911.73; TP391
计量
- 文章访问数: 1222
- HTML全文浏览量: 409
- PDF下载量: 231
- 被引次数: 0
出版历程
- 收稿日期: 2021-12-29
- 修回日期: 2022-03-19
- 网络出版日期: 2022-04-17
- 刊出日期: 2022-10-19

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

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)

摘要

摘要: 针对低光照条件下拍摄图像质量低下的问题，该文提出一种基于双重迭代的零样本低照度图像增强方法。其外层迭代通过卷积神经网络估计增强参数，再由内层迭代进行图像增强，增强结果进一步用于计算损失函数并反馈更新外层的参数估计网络，最终通过多轮迭代生成高质量的图像。在该框架下，还设计了多尺度增强系数估计模块、基于注意力的像素级大气光估计模块，并提出了基于亮度对比度、大气光、颜色均衡以及图像平滑性先验的无监督损失函数。大量实验结果表明，该方法可有效将低光照图像增强为高质量的清晰图像，其性能优于现有的同类方法。同时该方法基于零样本学习，不需任何训练数据集，具有良好的普适性。
- 图像增强 /
- 低照度 /
- 无监督学习 /
- 零样本学习 /
- 迭代增强
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
¹⁾ 夜间实拍图像测试集网址: https://github.com/Feng-wy/Zero-shot-dual-iter-LLE

HTML全文

图 1 双重迭代示意图

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图 2 多尺度增强系数估计模块(MCEM)结构图

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图 3 基于注意力的大气光与全局大气光效果对比图

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图 4 基于注意力的大气光值估计模块(ATEM)结构图

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图 5 使用不同窗口大小的反相亮通道结果对比

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图 6 不同方法在LOL-v2测试集上的效果对比

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图 7 不同方法在真实夜间图像上的效果对比

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图 8 不同照度下的低照度图像和增强结果

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图 9 不同损失函数处理后的可视化结果

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图 10 双重迭代次数与SSIM指标的关系统计图

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表 1 双重迭代流程

输入：低照度图像${J_0}$；初始权重${W_1}$；迭代次数$K$，$N$；学习率　　　　$\alpha $；迭代计数$n$=1，$k$；
While($n < N$)do：
载入权重${W_n}$；
计算：$ D_n^{{\text{out}}}({J_0},{W_n}) $，$ A_l^n = {\text{MCEM}}{}_l({J_0}) $，${E^n} = {\text{ATEM}}({J_0})$；　　　　初始化$k$=1;
While($k < K$)do：
计算：$ J_k^n = D_k^{{\text{in}}}(J_{k - 1}^n,A_l^n,{E^n}) $；
End While
经过$K$次内层迭代，第$n$次外层迭代的中间结果为：$J_K^n$ 　根据$J_K^n$计算损失函数${L_{{\text{total}}}}$和梯度$\dfrac{{\partial {L_{{\text{total}}}}}}{{\partial {W_n}}}$；
更新权重：${W_{n + 1}} = {W_n} - \alpha \cdot \dfrac{{\partial {L_{{\text{total}}}}}}{{\partial {W_n}}}$；
End While
输出：最终结果为$J_K^N$

下载: 导出CSV

表 2 多尺度增强系数估计模块(MCEM)网络参数表

模块	参数	下采样	多尺度特征融合	上采样
MCEM(l=1)	输出通道数	32, 32, 64	32, 32, 32, 32	32, 32, 3
MCEM(l=1)	激活函数	ReLU	ReLU, ReLU	ReLU, ReLU, Tanh
MCEM(l=2)	输出通道数	32,32,64	32, 32, 32, 32	32, 32, 3
MCEM(l=2)	激活函数	ReLU	ReLU, ReLU	ReLU, ReLU, Tanh
MCEM(l=3)	输出通道数	32,32,64	32, 32, 32, 32	32, 32, 3
MCEM(l=3)	激活函数	ReLU	ReLU, ReLU	ReLU, ReLU, Tanh

下载: 导出CSV

表 3 LOL-v2测试集上不同方法的客观评价指标

指标	LIME	RetinexNet	EnlightenGAN	Zero-DCE	RRDNet	本文方法
PSNR(dB)↑	18.06	16.26	18.52	18.94	16.23	20.68
SSIM↑	0.57	0.49	0.59	0.59	0.53	0.62
MAE↓	9.45	10.38	7.78	8.35	12.71	7.21
VGG16-PFS↓	1.07	1.66	1.04	0.98	1.53	0.91
CEIQ↑	2.99	3.07	3.16	2.91	2.64	3.15
NIQE↓	6.56	6.38	6.35	6.39	6.66	6.23
SSEQ↓	29.24	37.15	23.22	26.04	21.71	26.58

下载: 导出CSV

表 4 夜间实拍图像测试集上不同方法的客观评价指标

指标	LIME	RetinexNet	EnlightenGAN	Zero-DCE	RRDNet	本文方法
CEIQ↑	2.89	2.94	3.02	2.77	2.46	3.01
NIQE↓	6.28	6.11	7.43	6.35	6.86	6.06
SSEQ↓	14.58	27.13	18.90	14.31	16.54	12.73

下载: 导出CSV

表 5 消融实验结果

指标	$ \beta {L_{{\text{bc}}}} $	$\beta {L_{{\text{bc}}}} + \gamma {L_{{\text{cwb}}}}$	$\beta {L_{{\text{bc}}}} + \gamma {L_{{\text{cwb}}}} + \mu {L_{{\text{sm}}}}$	${L_{{\text{total}}}}$
PSNR(dB)	13.66	14.76	18.12	20.68
SSIM	0.40	0.48	0.57	0.62

下载: 导出CSV

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