RelightGAN: 基于生成对抗网络的暗图像增强
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河南省科技攻关项目(222102220050)


RelightGAN: Generative Adversarial Network for Dark Image Enhancement
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    摘要:

    针对成像设备在夜间或低光环境下拍摄图像出现光照不足、对比度低和信息丢失等问题. 设计基于生成对抗网络(generative adversarial network, GAN)改进的暗图像增强网络RelightGAN, 该网络包含两个判别器和一个生成器, 由两组对抗损失和循环损失共同约束生成器, 使之生成更优异的光照层. 为增强网络训练过程中对图像细节信息的恢复能力, 引入残差网络解决梯度消失问题, 同时引入混合注意力机制CBAM结构, 提升生成器对图像中重要信息和结构的关注度增强网络表达能力. 通过与其他暗图像增强网络增强后的暗图像进行对比, RelightGAN网络增强后的图像, 相较于其他网络峰值信噪比(PSNR)值提高了12.81%, 结构相似度(SSIM)值提高了5.95%. 实验结果表明RelightGAN网络结合了传统算法和神经网络的优点, 实现了暗场景图像的增强, 提高了图像可见度.

    Abstract:

    Aiming at the problems of insufficient light, low contrast, and information loss in images taken by imaging devices at night or in low-light environments, an improved dark image enhancement network named RelightGAN is designed based on generative adversarial network (GAN). It contains two discriminators and one generator, and the generator is jointly constrained by two sets of adversarial losses and cyclic losses to generate a better illumination layer. To enhance the recovery of image details during network training, a residual network is introduced to solve the gradient vanishing problem. At the same time, a hybrid attention mechanism CBAM structure is introduced to increase the generator’s attention to important information and structures in the image, enhancing network expression capability. By comparing the image enhanced by RelightGAN with those enhanced by other dark image enhancement networks, the peak signal-to-noise ratio (PSNR) of the former is improved by 12.81% and the structural similarity (SSIM) is enhanced by 5.95%. Experimental results show that the RelightGAN network combines the advantages of traditional algorithms and neural networks to improve dark scene images and image visibility.

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费致根,宋晓晓,郭兴,鲁豪. RelightGAN: 基于生成对抗网络的暗图像增强.计算机系统应用,2024,33(10):263-269

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  • 收稿日期:2024-04-09
  • 最后修改日期:2024-05-06
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  • 在线发布日期: 2024-08-21
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