Low-light image enhancement based on dual-residual convolutional network

Qing-jiang CHEN; Mei QU

doi:10.37188/CJLCD.2020-0168

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Low-light image enhancement based on dual-residual convolutional network

Image Processing | 更新时间：2021-02-07

- Low-light image enhancement based on dual-residual convolutional network
- Chinese Journal of Liquid Crystals and Displays Vol. 36, Issue 2, Pages: 305-316(2021)
- 作者机构：
  
  西安建筑科技大学理学院, 陕西西安 710055
- 作者简介：
- 基金信息：
- DOI：10.37188/CJLCD.2020-0168
  CLC： TP391.4
- Received：26 June 2020，
  
  Revised：08 September 2020，
  
  Accepted：08 September 2020，
  
  Published：2021-02
- 稿件说明：
移动端阅览
Qing-jiang CHEN, Mei QU. Low-light image enhancement based on dual-residual convolutional network[J]. Chinese journal of liquid crystals and displays, 2021, 36(2): 305-316.
DOI：

Qing-jiang CHEN, Mei QU. Low-light image enhancement based on dual-residual convolutional network[J]. Chinese journal of liquid crystals and displays, 2021, 36(2): 305-316. DOI： 10.37188/CJLCD.2020-0168.

摘要

为解决当前低照度图像增强问题，提出了一种基于双残差卷积网络的图像增强算法。首先，根据Retinex理论模型，将正常照度图像合成低照度图像，再分别将它们分解在R（红）、G（绿）、B（蓝）3个分量上，然后通过特征提取模块和双残差模块学习低照度图像与正常照度图像在各分量的映射关系，获得各分量上的增强图像，最后合成增强的RGB图像。采用双边滤波优化增强的RGB图像，使得所获得的图像更加接近参考图像。实验表明，本文所提算法，对于处理合成的低照度图像，峰值信噪比最高可达25.931 1 dB，结构相似度最高可达0.945 2；对于处理真实的低照度图像，盲图像质量评估指标高于其他算法，且运行速度更快。

Abstract

In order to solve the current problem about low-light image enhancement

an algorithm of image enhancement based on dual-residual convolutional network is proposed. First

according to Retinex theory

the normal-light image is synthesized into low-light image

the synthetic is decomposed onto the three components of R、G and B

and learning the mapping relations between low-light image and normal-light image on all components through the module of feature extraction as well as dual-residual. Then the enhanced image on all components can be obtained

and finally the enhanced RGB image is synthesized. Subsequently

the bilateral filtering is used to optimize the enhanced RGB image so that the obtained image is analogical to the reference image.The experiment results show that the algorithm proposed in this paper

whose Peak Signal to Noise Ratio can reach up to 25.931 1 dB and Structural Similarity Index can reach up to 0.945 2 in terms of processing synthesized low-light image

and whose novel blind image quality assessment can exceed other compared algorithms and the algorithm in this paper goes faster in terms of processing real low-light image.Therefore

the proposed algorithm is superior to the contrast algorithms.

关键词

Keywords

references

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