Li B, Peng X, Wang Z, et al. Aod-net: All-in-one dehazing network[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 4770-4778.
1. Overview
此前的大多数工作都是独立估计
- Transmission matrix
- Atmosphere light
但是,通过non-joint估计的这两个参数在同时使用时,可能会进一步放大误差。
- DehazeNet overestimate A and cause overexposure visual effect
因此论文
- 基于re-formulated atmosphere scattering model
- 提出AOD-Net,直接生成clean image
- AOD-Net可直接嵌入到其他模型中,如Faster R-CNN
- 0.026s to process 480x640 image with a single GPU
1.1. Re-formulated Atmosphere Scattering Model
- 基本物理模型
- Re-formulated
估计K(即联合估计A和t)
1.2. Model
1.3. 相关工作
- Maximizing the local contrast
- Estimate the albedo of the scene
- Dark channel prior
- Enforce the boundary constraint and contextual regularization
- Color attenuation prior and A linear model of scene depth
(Deep Learning)
- MSCNN. Multi-scale, coarse-to-fine transmission matrix
- DehazeNet. end-to-end for transmission estimation
1.4. 数据集
根据基本物理模型,通过设置不同的参数合成haze(数据集提供depth-meta)
- A. [0.6, 1.0],choosing each channel uniformly
β. {0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6}
NYU2 Depth Database
- 训练集. 27,256张图片
- TestSetA. 3,170张图片
- Middlebury Stereo Database
- TestSetB. 800张图片
- Natural Hazy Image
2. Experiments
2.1. Synthetic实验结果
- 图片可以分解两个元素的和
- Mean. 所有像素点的值设置为同一个均值,即与A有关的global illumination
- Residual. local structural variations and contrast
- 两张图片的MSE,可看作是Mean和Residual两部分的MSE
- 相比于local distortion,人眼对global illumination更敏感
- 实验证明,AOD-Net的Residual MSE与其他方法相近,但是Mean MSE更低
- 说明AOD-Net能够更好地recover A.
2.2. 运行时间
2.3. Natural实验结果
2.4. Anti-halation
2.5. 白色背景
2.6. 嵌入到Faster R-CNN中
雾霾程度
- Heavy. A=1, β=0.1
- Medium. A=1, β=0.06
- Light. A=1, β=0.04