Yang X, Xu Z, Luo J. Towards perceptual image dehazing by physics-based disentanglement and adversarial training[C]//Thirty-second AAAI conference on artificial intelligence. 2018.
1. Overview
现有的de-hazing方法
- on synthetic dataset
- hand-designed priors
- supervised training
因此，论文提出Disentangled Dehazing Network
- weakly-supervised
- GAN, multi-scale discriminator
- physical-model based disentanglement
- reconstruction
- collect HazyCity dataset
1.1. Related Work
- DehazeNet
- MSCNN
- AOD-Net
- CycleGAN
- dualGAN
- AIGN
- WaterGAN
- discoGAN
- UNIT
1.2. 模型
1.2.1. Generator
- G_J. 生成clean image
- G_A.生成atmosphere light
- G_t. 生成transmission map
1.2.2. Discriminator
multi-scale结构
- local discriminator (感知域70x70). Model high-frequency structure (texture/style recognition)
- global discriminator (感知域256x256). Global information, alleviate artifacts
1.3. Reconstruction
Reconstruction Loss
Adversarial Loss
Regularization Loss
the smoothness of the medium transmission map.
1.4. Recovering Method of Haze-free Image
- The output of G_J
- Generate from A and t
- Combine
1.5. 数据集
- D-HAZY (synthetic)
- β=1, A=255.
- NYU-Depth (23 images).
- Middlebury (1449 images).
- HazyCity (real)
- natural, outdoor. Build on PM25 dataset.
- hazy (845), haze-free (1891)
- crawled from tourist website and photos of various attraction sites and street scenes in Beijing. 三个标注者，选取标注一致的图片。
1.6. Future Work
- de-raining
- image matting
2. Experiments
2.1. Baseline
2.1.1. prior-based
- DCP
- CAP
- NCP
2.1.2. learning-based
- DehazeNet
- MSCNN
- CycleGAN
2.2. Metric
- PSNR
- SSIM
- CIEDE2000. measure color difference
2.3. 实验结果
2.4. Ablation Study
