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Underwater image enhancement based on Inception-Residual and generative adversarial network
- Chinese Journal of Liquid Crystals and Displays Vol. 36, Issue 11, Pages: 1474-1485(2021)
- 作者机构：
上海海洋大学 信息学院, 上海 201306
- 作者简介：
- 基金信息：Supported by National Natural Science Foundation of China(41776142)
DOI：10.37188/CJLCD.2021-0058
CLC： TP391.41Received：01 March 2021，
Revised：29 March 2021，
Published：2021-11
- 稿件说明：
移动端阅览
De-xing WANG, Yue WANG, Hong-chun YUAN. Underwater image enhancement based on Inception-Residual and generative adversarial network[J]. Chinese journal of liquid crystals and displays, 2021, 36(11): 1474-1485.
De-xing WANG, Yue WANG, Hong-chun YUAN. Underwater image enhancement based on Inception-Residual and generative adversarial network[J]. Chinese journal of liquid crystals and displays, 2021, 36(11): 1474-1485. DOI： 10.37188/CJLCD.2021-0058.
摘要
为解决光在水下传播过程中由吸收与散射效应导致的水下图像模糊、对比度低和颜色失真问题，提出一种基于Inception-Residual和生成对抗网络的水下图像增强算法。首先，将退化水下图像缩放至256×256×3大小，以获得用于训练模型的数据集。接着，将Inception模块、残差思想、编码解码结构和生成对抗网络相结合，构建IRGAN (Generative Adversarial Network with Inception-Residual)模型来增强水下图像。然后，利用全局相似性、内容感知和色彩感知构造多项损失函数，约束生成网络和判别网络的对抗训练。最后，通过训练好的模型对退化水下图像进行处理以获得清晰的水下图像。实验结果表明：与现有增强方法相比，所提算法增强的水下图像在PSNR、UIQM和IE指标上的平均值分别比第二名提升13.6%、4.1%和0.9%。在主观感知和客观评估中，增强后的水下图像在清晰度、对比度增强和颜色校正方面均得到改善。
Abstract
To solve the blurring
low contrast and color distortion problem of underwater image caused by light absorption and scattering effects in the underwater environment
an underwater image enhancement algorithm based on the Inception-Residual and generative adversarial network is proposed. Firstly
the degraded underwater image is scaled to a size of 256×256×3 to obtain a data set for the training model. The Inception module
residual idea
encoding and decoding structure and generative adversarial network are combined to build an IRGAN(Generative Adversarial Network with Inception-Residual) model to enhance underwater images. Then
a multi-loss function including global similarity
content perception and color perception is constructed to constrain the antagonistic training of generative network and discriminant network. Finally
the degraded underwater image is processed by the trained model to obtain a clear underwater image. The experimental results show that
compared with the existing enhancement methods
the average values of the PSNR
UIQM and IE indicators of the underwater images enhanced by the proposed algorithm are improved by 13.6%
4.1% and 0.9%
respectively
compared with the second place. In subjective perception and objective evaluation
the sharpness
contrast enhancement and color correction of the enhanced underwater image are improved.
关键词
Keywords
references
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