Dense Scene Information Estimation Network for Dehazing_CVPR2019
Abstract 通常情况下，除雾算法都会求大气光A和透射图 t ,而后根据去雾模型公式反推得到去雾图像J，在本文中，针对这种去雾方法提出了两个网络，第一个是At-DH，基于DenseNet编码器与两个不同的基于DenseNet的解码器，共同估计去雾图像；第二个网络为AtJ-DH网络，在At-DH的基础上，增加了一个基于DenseNet的解码器，用以与A、t一起重新创建无雾图像，通过自定
相关文章
- 1. OCNet_Object Context Network for Scene parsing
- 2. 《Dense Residual Network for Retinal Vessel Segmentation》
- 3. Dual attention network for scene segmentation
- 4. Residual Dense Network for Image Super-Resolution
- 5. Dual Attention Network for Scene Segmentation
- 6. ACNet_ Adaptive Context Network for Scene Parsing
- 7. DANet: Dual Attention Network for Scene Segmentation
- 8. Spatial Attention Network for Scene Parsing
- 9. Stacked Hourglass Network for Human Pose Estimation
- 10. Paper | Residual Dense Network for Image Super-Resolution
- 更多相关文章...
- • Swift for 循环 - Swift 教程
- • Scala for循环 - Scala教程
- • Flink 数据传输及反压详解
- • Composer 安装与使用
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. OCNet_Object Context Network for Scene parsing
- 2. 《Dense Residual Network for Retinal Vessel Segmentation》
- 3. Dual attention network for scene segmentation
- 4. Residual Dense Network for Image Super-Resolution
- 5. Dual Attention Network for Scene Segmentation
- 6. ACNet_ Adaptive Context Network for Scene Parsing
- 7. DANet: Dual Attention Network for Scene Segmentation
- 8. Spatial Attention Network for Scene Parsing
- 9. Stacked Hourglass Network for Human Pose Estimation
- 10. Paper | Residual Dense Network for Image Super-Resolution