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基于改进循环生成对抗网络的低照度图像增强

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为了解决在低照度图像增强过程中配对数据集获取困难,且经过增强后的图像质量不佳的问题,通过改进循环生成对抗网络模型的方法研究了非配对低照度图像增强的实现.生成器部分采用融合了Vision Transformer结构的U-NET模型替代原始的生成器模型,来提高图像变换的周期一致性和内容保持性,并有效地处理图像研究中普遍存在的长距离空间相关性的问题.判别器部分针对图像研究的特点选择PatchGAN代替传统的判别器,提高对图像细节的判别能力.同时引入身份一致性损失函数,提高图像质量.结果表明,相较于传统方法,本文改进的模型有着更好的主观视觉效果,同时在客观评价指标也有着相应的提高,可见本文改进模型的有效性.
Low-light Image Enhancement Based on Improved Cycle Generative Adversarial Network
In order to solve the problems of difficulty in obtaining paired data sets and poor quality of enhanced images in the process of low-light image enhancement,the implementation of unpaired low-light image enhancement was studied by improving the cy-cle generative adversarial network model.In the generator part,a U-NET model integrated with the Vision Transformer structure was used to replace the original generator model,so as to improve the cycle consistency and content retention of image transformation,and effectively deal with the problem of long-distance spatial correlation commonly existing in image research.In the discriminator part,PatchGAN was selected to replace the traditional discriminator according to the characteristics of image research to improve the discrim-ination ability of image details.At the same time,the identity consistency loss function was introduced to improve the image quality.The results show that compared with the traditional method,the improved model in this paper has better subjective visual effect,and al-so has a corresponding improvement in the objective evaluation index.It can be seen that the improved model in this paper is effective.

deep learningimage enhancementlow-light image enhancementcycle generative adversarial networkvision transformer

隋涛、吴森炜、贾浩、万可欣、杨洋

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山东科技大学电气与自动化工程学院,青岛 266000

深度学习 图像增强 低光图像增强 循环生成对抗网络 Vision Transformer

国家自然科学基金面上项目教育部协同育人项目教育部协同育人项目山东省高等学校科研项目

62273214220900782082623220703873055114J18KA317

2024

10.12404/j.issn.1671-1815.2304161

科学技术与工程
中国技术经济学会

科学技术与工程

CSTPCD北大核心
影响因子：0.338
ISSN：1671-1815
年,卷(期)：2024.24(14)