Zhang Shengdong, Zhao Liping, Hu Keli, Feng Sheng, Fan En, Zhao Li
Key Laboratory of Intelligent Informatics for Safety and Emergency of Zhejiang Province, Wenzhou University, Education Park Zone, Wenzhou City, 325035, Zhejiang Province, People's Republic of China.
Department of Computer Science and Engineering, Shaoxing University, Yuecheng District, Shaoxing City, 312000, Zhejiang Province, People's Republic of China.
Sci Rep. 2023 Sep 15;13(1):15333. doi: 10.1038/s41598-023-41561-z.
Single image dehazing has received a lot of concern and achieved great success with the help of deep-learning models. Yet, the performance is limited by the local limitation of convolution. To address such a limitation, we design a novel deep learning dehazing model by combining the transformer and guided filter, which is called as Deep Guided Transformer Dehazing Network. Specially, we address the limitation of convolution via a transformer-based subnetwork, which can capture long dependency. Haze is dependent on the depth, which needs global information to compute the density of haze, and removes haze from the input images correctly. To restore the details of dehazed result, we proposed a CNN sub-network to capture the local information. To overcome the slow speed of the transformer-based subnetwork, we improve the dehazing speed via a guided filter. Extensive experimental results show consistent improvement over the state-of-the-art dehazing on natural haze and simulated haze images.
单图像去雾受到了广泛关注，并借助深度学习模型取得了巨大成功。然而，其性能受到卷积局部局限性的限制。为了解决这一局限性，我们通过结合Transformer和引导滤波器设计了一种新颖的深度学习去雾模型，即深度引导Transformer去雾网络。具体而言，我们通过基于Transformer的子网来解决卷积的局限性，该子网可以捕捉长距离依赖关系。雾霾依赖于深度，这需要全局信息来计算雾霾密度，并正确地从输入图像中去除雾霾。为了恢复去雾结果的细节，我们提出了一个卷积神经网络子网来捕捉局部信息。为了克服基于Transformer的子网速度慢的问题，我们通过引导滤波器提高去雾速度。大量实验结果表明，相对于自然雾霾和模拟雾霾图像上的现有去雾方法，该方法有持续的改进。
