Single Image Dehazing via Conditional Generative Adversarial Network
原文 貢獻 提出了一種基於條件生成對抗神經網絡的去霧網絡 生成網絡採用編碼器——解碼器的結構，以捕獲更多有用信息 新的損失函數，包括： 合成包括室內和室外的有霧圖像數據集。 生成網絡的結構 生成網絡是輸入有霧圖像生成清晰圖像，因此不僅要保留圖像的結構和細節還要去霧。受ResNet和U-Net啓發，在生成網絡由編碼器和解碼器組成，使用對稱層的跳過連接(skip connection)來突破解碼過程中
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相關文章
- 1. 論文閱讀：Dehaze-GLCGAN: Unpaired Single Image Dehazing Via Adversarial Training
- 2. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORK
- 3. Conditional Generative Adversarial Nets
- 4. Generative Adversarial Text to Image Synthesis
- 5. A Cascaded Convolutional Neural Network for Single Image Dehazing
- 6. Multimodal——Paper簡讀筆記：Multimodal Image-to-Image Translation via a Single Generative Adversarial Net
- 7. Conditional Generative Adversarial Nets（CGAN）
- 8. Attentive Generative Adversarial Network for Raindrop Removal from A Single Image 閱讀筆記
- 9. 論文解讀《Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network》SRGAN
- 10. 論文閱讀——Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network