Single Image Dehazing via Conditional Generative Adversarial Network
原文 贡献 提出了一种基于条件生成对抗神经网络的去雾网络 生成网络采用编码器——解码器的结构，以捕获更多有用信息 新的损失函数，包括： 合成包括室内和室外的有雾图像数据集。 生成网络的结构 生成网络是输入有雾图像生成清晰图像，因此不仅要保留图像的结构和细节还要去雾。受ResNet和U-Net启发，在生成网络由编码器和解码器组成，使用对称层的跳过连接(skip connection)来突破解码过程中
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相关文章
- 1. 论文阅读：Dehaze-GLCGAN: Unpaired Single Image Dehazing Via Adversarial Training
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- 4. Generative Adversarial Text to Image Synthesis
- 5. A Cascaded Convolutional Neural Network for Single Image Dehazing
- 6. Multimodal——Paper简读笔记：Multimodal Image-to-Image Translation via a Single Generative Adversarial Net
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