文献类型：期刊文献

英文题名：Deep guided transformer dehazing network

作者：Zhang, Shengdong[1,2];Zhao, Liping[2];Hu, Keli[2];Feng, Sheng[2];Fan, En[2];Zhao, Li[1]

机构：[1]Wenzhou Med Univ, Key Lab Watershed Sci & Hlth Zhejiang Prov, Educ Pk Zone, Wenzhou 325035, Zhejiang, Peoples R China;[2]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Zhejiang, Peoples R China

年份：2023

卷号：13

期号：1

外文期刊名：SCIENTIFIC REPORTS

收录：SCI-EXPANDED(收录号：WOS:001068962200006)、、Scopus(收录号：2-s2.0-85171345863)、WOS

基金：This work is supported by the National Natural Science Foundation of China (Nos. 62101387, 62271321). This work is partially supported by the Science Project of Shaoxing University (Nos. 20205048, 20210026, and 2022LG006), and in part by the Science and Technology Plan Project in Basic Public Welfare class of Shaoxing city (No.2022A11002).

语种：英文

外文摘要：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.