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A Novel Transformer-Based Attention Network for Image Dehazing
Image dehazing is challenging due to the problem of ill-posed parameter estimation. Numerous prior-based and learning-based methods have achieved great success. However, most learning-based methods use the changes and connections between scale and depth in convolutional neural networks for feature e...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105677/ https://www.ncbi.nlm.nih.gov/pubmed/35591118 http://dx.doi.org/10.3390/s22093428 |
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author | Gao, Guanlei Cao, Jie Bao, Chun Hao, Qun Ma, Aoqi Li, Gang |
author_facet | Gao, Guanlei Cao, Jie Bao, Chun Hao, Qun Ma, Aoqi Li, Gang |
author_sort | Gao, Guanlei |
collection | PubMed |
description | Image dehazing is challenging due to the problem of ill-posed parameter estimation. Numerous prior-based and learning-based methods have achieved great success. However, most learning-based methods use the changes and connections between scale and depth in convolutional neural networks for feature extraction. Although the performance is greatly improved compared with the prior-based methods, the performance in extracting detailed information is inferior. In this paper, we proposed an image dehazing model built with a convolutional neural network and Transformer, called Transformer for image dehazing (TID). First, we propose a Transformer-based channel attention module (TCAM), using a spatial attention module as its supplement. These two modules form an attention module that enhances channel and spatial features. Second, we use a multiscale parallel residual network as the backbone, which can extract feature information of different scales to achieve feature fusion. We experimented on the RESIDE dataset, and then conducted extensive comparisons and ablation studies with state-of-the-art methods. Experimental results show that our proposed method effectively improves the quality of the restored image, and it is also better than the existing attention modules in performance. |
format | Online Article Text |
id | pubmed-9105677 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91056772022-05-14 A Novel Transformer-Based Attention Network for Image Dehazing Gao, Guanlei Cao, Jie Bao, Chun Hao, Qun Ma, Aoqi Li, Gang Sensors (Basel) Article Image dehazing is challenging due to the problem of ill-posed parameter estimation. Numerous prior-based and learning-based methods have achieved great success. However, most learning-based methods use the changes and connections between scale and depth in convolutional neural networks for feature extraction. Although the performance is greatly improved compared with the prior-based methods, the performance in extracting detailed information is inferior. In this paper, we proposed an image dehazing model built with a convolutional neural network and Transformer, called Transformer for image dehazing (TID). First, we propose a Transformer-based channel attention module (TCAM), using a spatial attention module as its supplement. These two modules form an attention module that enhances channel and spatial features. Second, we use a multiscale parallel residual network as the backbone, which can extract feature information of different scales to achieve feature fusion. We experimented on the RESIDE dataset, and then conducted extensive comparisons and ablation studies with state-of-the-art methods. Experimental results show that our proposed method effectively improves the quality of the restored image, and it is also better than the existing attention modules in performance. MDPI 2022-04-30 /pmc/articles/PMC9105677/ /pubmed/35591118 http://dx.doi.org/10.3390/s22093428 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gao, Guanlei Cao, Jie Bao, Chun Hao, Qun Ma, Aoqi Li, Gang A Novel Transformer-Based Attention Network for Image Dehazing |
title | A Novel Transformer-Based Attention Network for Image Dehazing |
title_full | A Novel Transformer-Based Attention Network for Image Dehazing |
title_fullStr | A Novel Transformer-Based Attention Network for Image Dehazing |
title_full_unstemmed | A Novel Transformer-Based Attention Network for Image Dehazing |
title_short | A Novel Transformer-Based Attention Network for Image Dehazing |
title_sort | novel transformer-based attention network for image dehazing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105677/ https://www.ncbi.nlm.nih.gov/pubmed/35591118 http://dx.doi.org/10.3390/s22093428 |
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