Deep guided transformer dehazing network.
Sci Rep
; 13(1): 15333, 2023 Sep 15.
Article
en En
| MEDLINE
| ID: mdl-37714880
ABSTRACT
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.
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Colección:
01-internacional
Banco de datos:
MEDLINE
Idioma:
En
Revista:
Sci Rep
Año:
2023
Tipo del documento:
Article