Super-resolution reconstruction based on Gaussian transform and attention mechanism
Image super-resolution reconstruction can reconstruct low resolution blurred images in the same scene into high-resolution images. Combined with multi-scale Gaussian difference transform, attention mechanism and feedback mechanism are introduced to construct a new super-resolution reconstruction net...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2023-01-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1182.pdf |
| _version_ | 1797952933797560320 |
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| author | Shuilong Zou Mengmu Ruan Xishun Zhu Wenfang Nie |
| author_facet | Shuilong Zou Mengmu Ruan Xishun Zhu Wenfang Nie |
| author_sort | Shuilong Zou |
| collection | DOAJ |
| description | Image super-resolution reconstruction can reconstruct low resolution blurred images in the same scene into high-resolution images. Combined with multi-scale Gaussian difference transform, attention mechanism and feedback mechanism are introduced to construct a new super-resolution reconstruction network. Three improvements are made. Firstly, its multi-scale Gaussian difference transform can strengthen the details of low resolution blurred images. Secondly, it introduces the attention mechanism and increases the network depth to better express the high-frequency features. Finally, pixel loss function and texture loss function are used together, focusing on the learning of structure and texture respectively. The experimental results show that this method is superior to the existing methods in quantitative and qualitative indexes, and promotes the recovery of high-frequency detail information. |
| first_indexed | 2024-04-10T22:53:54Z |
| format | Article |
| id | doaj.art-9a545273167646adba588d35c2ff7035 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-10T22:53:54Z |
| publishDate | 2023-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-9a545273167646adba588d35c2ff70352023-01-14T15:05:08ZengPeerJ Inc.PeerJ Computer Science2376-59922023-01-019e118210.7717/peerj-cs.1182Super-resolution reconstruction based on Gaussian transform and attention mechanismShuilong Zou0Mengmu Ruan1Xishun Zhu2Wenfang Nie3Nanchang Normal College of Applied Technology, School of Electronic and Information Engineering, Nanchang, Jiangxi, ChinaNanchang Institute of Science & Technology, School of Wealth Management, Nanchang, Jiangxi, ChinaNanchang Normal College of Applied Technology, School of Electronic and Information Engineering, Nanchang, Jiangxi, ChinaCurrent Affiliation: School of Economics and Management, Jiangxi Manufacturing Polytechnic College, Nanchang, ChinaImage super-resolution reconstruction can reconstruct low resolution blurred images in the same scene into high-resolution images. Combined with multi-scale Gaussian difference transform, attention mechanism and feedback mechanism are introduced to construct a new super-resolution reconstruction network. Three improvements are made. Firstly, its multi-scale Gaussian difference transform can strengthen the details of low resolution blurred images. Secondly, it introduces the attention mechanism and increases the network depth to better express the high-frequency features. Finally, pixel loss function and texture loss function are used together, focusing on the learning of structure and texture respectively. The experimental results show that this method is superior to the existing methods in quantitative and qualitative indexes, and promotes the recovery of high-frequency detail information.https://peerj.com/articles/cs-1182.pdfSuper-resolution reconstructionMulti-scaleGaussian difference transformAttention mechanism |
| spellingShingle | Shuilong Zou Mengmu Ruan Xishun Zhu Wenfang Nie Super-resolution reconstruction based on Gaussian transform and attention mechanism PeerJ Computer Science Super-resolution reconstruction Multi-scale Gaussian difference transform Attention mechanism |
| title | Super-resolution reconstruction based on Gaussian transform and attention mechanism |
| title_full | Super-resolution reconstruction based on Gaussian transform and attention mechanism |
| title_fullStr | Super-resolution reconstruction based on Gaussian transform and attention mechanism |
| title_full_unstemmed | Super-resolution reconstruction based on Gaussian transform and attention mechanism |
| title_short | Super-resolution reconstruction based on Gaussian transform and attention mechanism |
| title_sort | super resolution reconstruction based on gaussian transform and attention mechanism |
| topic | Super-resolution reconstruction Multi-scale Gaussian difference transform Attention mechanism |
| url | https://peerj.com/articles/cs-1182.pdf |
| work_keys_str_mv | AT shuilongzou superresolutionreconstructionbasedongaussiantransformandattentionmechanism AT mengmuruan superresolutionreconstructionbasedongaussiantransformandattentionmechanism AT xishunzhu superresolutionreconstructionbasedongaussiantransformandattentionmechanism AT wenfangnie superresolutionreconstructionbasedongaussiantransformandattentionmechanism |