論文閱讀：Learning to Dehaze From Realistic Scene with A Fast Physics Based Dehazing Network
代碼：https://github.com/liruoteng/3DRealisticSceneDehaze（未公佈） 1. 摘要 基於學習的方法需要有霧圖和ground-truth作爲學習的數據集，但是通常不太可能捕捉到真實的這類數據。現在許多研究通過使用大氣散射模型在普通RGBD數據集上合成霧圖和ground-truth對的數據集。然而，由於高質量深度的大型數據集大多是室內的，室外的深度圖不精
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相關文章
- 1. A Gift from Knowledge Distillation: Fast Optimization,Network Minimization and Transfer Learning論文初讀
- 2. 論文閱讀：Dehaze-GLCGAN: Unpaired Single Image Dehazing Via Adversarial Training
- 3. 【論文解讀】Pixel-Anchor: A Fast Oriented Scene Text Detector with Combined Networks
- 4. 論文閱讀：GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing
- 5. 論文閱讀筆記《Few-Shot Learning with Localization in Realistic Settings》
- 6. 論文閱讀：Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing
- 7. 論文閱讀：Fast R-CNN
- 8. 論文閱讀：AutoAugment: Learning Augmentation Strategies from Data
- 9. 【論文閱讀】Sequence to Sequence Learning with Neural Network
- 10. [論文閱讀]Object Detection With Deep Learning: A Review