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Multi-Stage Attentive Network for Motion Deblurring via Binary Cross-Entropy Loss
In this paper, we present the multi-stage attentive network (MSAN), an efficient and good generalization performance convolutional neural network (CNN) architecture for motion deblurring. We build a multi-stage encoder–decoder network with self-attention and use the binary cross-entropy loss to trai...
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/PMC9601862/ https://www.ncbi.nlm.nih.gov/pubmed/37420434 http://dx.doi.org/10.3390/e24101414 |
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author | Guo, Cai Chen, Xinan Chen, Yanhua Yu, Chuying |
author_facet | Guo, Cai Chen, Xinan Chen, Yanhua Yu, Chuying |
author_sort | Guo, Cai |
collection | PubMed |
description | In this paper, we present the multi-stage attentive network (MSAN), an efficient and good generalization performance convolutional neural network (CNN) architecture for motion deblurring. We build a multi-stage encoder–decoder network with self-attention and use the binary cross-entropy loss to train our model. In MSAN, there are two core designs. First, we introduce a new attention-based end-to-end method on top of multi-stage networks, which applies group convolution to the self-attention module, effectively reducing the computing cost and improving the model’s adaptability to different blurred images. Secondly, we propose using binary cross-entropy loss instead of pixel loss to optimize our model to minimize the over-smoothing impact of pixel loss while maintaining a good deblurring effect. We conduct extensive experiments on several deblurring datasets to evaluate the performance of our solution for deblurring. Our MSAN achieves superior performance while also generalizing and compares well with state-of-the-art methods. |
format | Online Article Text |
id | pubmed-9601862 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96018622022-10-27 Multi-Stage Attentive Network for Motion Deblurring via Binary Cross-Entropy Loss Guo, Cai Chen, Xinan Chen, Yanhua Yu, Chuying Entropy (Basel) Article In this paper, we present the multi-stage attentive network (MSAN), an efficient and good generalization performance convolutional neural network (CNN) architecture for motion deblurring. We build a multi-stage encoder–decoder network with self-attention and use the binary cross-entropy loss to train our model. In MSAN, there are two core designs. First, we introduce a new attention-based end-to-end method on top of multi-stage networks, which applies group convolution to the self-attention module, effectively reducing the computing cost and improving the model’s adaptability to different blurred images. Secondly, we propose using binary cross-entropy loss instead of pixel loss to optimize our model to minimize the over-smoothing impact of pixel loss while maintaining a good deblurring effect. We conduct extensive experiments on several deblurring datasets to evaluate the performance of our solution for deblurring. Our MSAN achieves superior performance while also generalizing and compares well with state-of-the-art methods. MDPI 2022-10-03 /pmc/articles/PMC9601862/ /pubmed/37420434 http://dx.doi.org/10.3390/e24101414 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 Guo, Cai Chen, Xinan Chen, Yanhua Yu, Chuying Multi-Stage Attentive Network for Motion Deblurring via Binary Cross-Entropy Loss |
title | Multi-Stage Attentive Network for Motion Deblurring via Binary Cross-Entropy Loss |
title_full | Multi-Stage Attentive Network for Motion Deblurring via Binary Cross-Entropy Loss |
title_fullStr | Multi-Stage Attentive Network for Motion Deblurring via Binary Cross-Entropy Loss |
title_full_unstemmed | Multi-Stage Attentive Network for Motion Deblurring via Binary Cross-Entropy Loss |
title_short | Multi-Stage Attentive Network for Motion Deblurring via Binary Cross-Entropy Loss |
title_sort | multi-stage attentive network for motion deblurring via binary cross-entropy loss |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601862/ https://www.ncbi.nlm.nih.gov/pubmed/37420434 http://dx.doi.org/10.3390/e24101414 |
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