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首页> 外文会议>International Conference on Computer Vision, Image and Deep Learning >Single image dehazing algorithm using generative adversarial network based on feature pyramid network
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Single image dehazing algorithm using generative adversarial network based on feature pyramid network

机译：基于特征金字塔网络的生成敌对网络的单个图像去吸收算法

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This paper proposes a single image dehazing algorithm using generative adversarial network (GAN) based on feature pyramid network (FPN). This method is an end-to-end image dehazing method, avoiding the physical model dependence. The generator uses MobileNet-V2 as the backbone network, and uses the FPN structure to improve the feature utilization rate of the image, combined with the discriminator formed by the convolutional neural network to form GAN that can improve the training stability and convergence of the generator. The model uses a lightweight MobileNet-V2 network, and the FPN structure also enables multiple-scale feature maps to be obtained while avoiding the use of direct scaling, thus reducing the computational power and memory requirements and allowing the model to operate with limited computational resources. We used the RESIDE training set to train our proposed model and conducted extensive experiments on the test set. The experimental results show that the algorithm has satisfactory results in terms of quality and speed.
机译：本文提出了一种基于特征金字塔网络（FPN）的生成对抗网络（GAN）的单个图像去吸收算法。该方法是端到端图像脱离方法，避免了物理模型依赖性。发电机使用MobileNet-V2作为骨干网，并使用FPN结构来改善图像的特征利用率，与由卷积神经网络形成的鉴别器相结合，以形成GaN，可以改善发电机的训练稳定性和收敛性。该模型使用轻量级MobileNet-V2网络，FPN结构还可以在避免使用直接缩放的同时获得多尺度特征映射，从而降低计算功率和存储器要求并允许模型与有限的计算资源一起运行。我们使用驻留的培训集来培训我们所提出的模型，并对测试集进行广泛的实验。实验结果表明，该算法在质量和速度方面具有令人满意的结果。

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