计算机科学
瓶颈
人工智能
卷积神经网络
大气(单位)
比例(比率)
网格
图像(数学)
图像处理
维数(图论)
计算机视觉
模式识别(心理学)
嵌入式系统
热力学
量子力学
物理
数学
纯数学
几何学
作者
Xiaohong Liu,Yongrui Ma,Zhihao Shi,Jun Chen
出处
期刊:International Conference on Computer Vision
日期:2019-10-01
被引量:438
标识
DOI:10.1109/iccv.2019.00741
摘要
We propose an end-to-end trainable Convolutional Neural Network (CNN), named GridDehazeNet, for single image dehazing. The GridDehazeNet consists of three modules: pre-processing, backbone, and post-processing. The trainable pre-processing module can generate learned inputs with better diversity and more pertinent features as compared to those derived inputs produced by hand-selected pre-processing methods. The backbone module implements a novel attention-based multi-scale estimation on a grid network, which can effectively alleviate the bottleneck issue often encountered in the conventional multi-scale approach. The post-processing module helps to reduce the artifacts in the final output. Experimental results indicate that the GridDehazeNet outperforms the state-of-the-arts on both synthetic and real-world images. The proposed hazing method does not rely on the atmosphere scattering model, and we provide an explanation as to why it is not necessarily beneficial to take advantage of the dimension reduction offered by the atmosphere scattering model for image dehazing, even if only the dehazing results on synthetic images are concerned.
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