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基于深度卷积神经网络的无参考低照度图像增强

陈勇 陈东 刘焕淋 黄美永 汪波

陈勇, 陈东, 刘焕淋, 黄美永, 汪波. 基于深度卷积神经网络的无参考低照度图像增强[J]. 电子与信息学报, 2022, 44(6): 2166-2174. doi: 10.11999/JEIT210386
引用本文: 陈勇, 陈东, 刘焕淋, 黄美永, 汪波. 基于深度卷积神经网络的无参考低照度图像增强[J]. 电子与信息学报, 2022, 44(6): 2166-2174. doi: 10.11999/JEIT210386
CHEN Yong, CHEN Dong, LIU Huanlin, HUANG Meiyong, WANG Bo. Unreferenced Low-lighting Image Enhancement Based on Deep Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(6): 2166-2174. doi: 10.11999/JEIT210386
Citation: CHEN Yong, CHEN Dong, LIU Huanlin, HUANG Meiyong, WANG Bo. Unreferenced Low-lighting Image Enhancement Based on Deep Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(6): 2166-2174. doi: 10.11999/JEIT210386

基于深度卷积神经网络的无参考低照度图像增强

doi: 10.11999/JEIT210386
基金项目: 国家自然科学基金(51977021),重庆市技术创新与应用发展专项(cstc2019jscx-mbdX0004)
详细信息
    作者简介:

    陈勇:男,1963年生,教授,博士,研究方向为图像处理与模式识别

    陈东:男,1995年生,硕士,研究方向为图像处理

    刘焕淋:女,1970年生,教授,博士生导师,研究方向为信息处理

    黄美永:女,1997年生,硕士生,研究方向为图像处理

    汪波:男,1995年生,硕士生,研究方向为图像处理

    通讯作者:

    陈勇 chenyong@cqupt.edu.cn

  • 中图分类号: TN911.73; TP391.41

Unreferenced Low-lighting Image Enhancement Based on Deep Convolutional Neural Network

Funds: The National Natural Science Foundation of China (51977021), Chongqing Key Technology Innovation Project (cstc2019jscx-mbdX0004)
  • 摘要: 针对低照度图像增强算法在实现细节增强的同时对噪声抑制考虑的不足问题,该文提出一种基于深度卷积神经网络的无参考低照度图像增强方法。首先,基于Retinex理论从输入的低照度图像中提取照射分量和反射分量,并分别对二者进行优化,随后将优化后的照射分量和反射分量相乘得到增强后的图像;同时,将3D块匹配(BM3D)的去噪效果融合进反射分量的优化过程中;最后,采用无参考图像训练的方式,并配合改进后的趋势一致性损失对网络参数进行更新。实验结果表明,该文算法相较于现有的主流算法,可有效地提升低照度图像的对比度和亮度,同时保持图像的自然性。
  • 图  1  网络整体框架

    图  2  分解网络

    图  3  反射分量优化网络

    图  4  不同光照下图像同一位置的梯度变化趋势

    图  5  不同算法对于在合成的低照度图像的处理结果(1)

    图  6  不同算法对于在合成的低照度图像的处理结果(2)

    图  7  不同算法对于真实低照度图像的处理结果

    图  8  本文算法在测试集上的各项指标得分分布情况

    表  1  不同算法处理结果的客观评价指标得分

    评价指标Dong[15]SRIE[6]LIME[7]RetinexGAN[12]本文算法
    PSNR15.7032513.12215.906216.48216.41
    SSIM0.57120.50590.57480.44470.62
    NIQE8.92177.3248.318.51347.721
    下载: 导出CSV

    表  2  算法处理时间对比(s)

    图像大小Dong[15]SRIE[6]LIME[7]RetinexGAN[12]本文算法
    256×2562.2951.7610.1422.2972.056
    480×3204.9422.7070.1753.7013.875
    720×54012.2565.5590.2728.7668.986
    下载: 导出CSV

    表  3  不同算法在LOL数据集上的测试结果

    评价指标Dong[15]SRIE[6]LIME[7]RetinexGAN[12]本文算法
    PSNR16.12011.855116.92016.57416.863
    SSIM0.6930.4950.5040.4570.711
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-05-07
  • 修回日期:  2022-03-21
  • 网络出版日期:  2022-03-24
  • 刊出日期:  2022-06-21

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