IEICE TRANSACTIONS on Information
Lightweight and Fast Low-Light Image Enhancement Method Based on PoolFormer
Summary :
Images captured in low-light environments have low visibility and high noise, which will seriously affect subsequent visual tasks such as target detection and face recognition. Therefore, low-light image enhancement is of great significance in obtaining high-quality images and is a challenging problem in computer vision tasks. A low-light enhancement model, LLFormer, based on the Vision Transformer, uses axis-based multi-head self-attention and a cross-layer attention fusion mechanism to reduce the complexity and achieve feature extraction. This algorithm can enhance images well. However, the calculation of the attention mechanism is complex and the number of parameters is large, which limits the application of the model in practice. In response to this problem, a lightweight module, PoolFormer, is used to replace the attention module with spatial pooling, which can increase the parallelism of the network and greatly reduce the number of model parameters. To suppress image noise and improve visual effects, a new loss function is constructed for model optimization. The experiment results show that the proposed method not only reduces the number of parameters by 49%, but also performs better in terms of image detail restoration and noise suppression compared with the baseline model. On the LOL dataset, the PSNR and SSIM were 24.098dB and 0.8575 respectively. On the MIT-Adobe FiveK dataset, the PSNR and SSIM were 27.060dB and 0.9490. The evaluation results on the two datasets are better than the current mainstream low-light enhancement algorithms.
- Publication
- IEICE TRANSACTIONS on Information Vol.E107-D No.1 pp.157-160
- Publication Date
- 2024/01/01
- Publicized
- 2023/10/05
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2023EDL8051
- Type of Manuscript
- LETTER
- Category
- Image Processing and Video Processing
Authors
Xin HU
Beijing Union University
Jinhua WANG
Beijing Union University
Sunhan XU
Beijing Union University
Keyword
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Xin HU, Jinhua WANG, Sunhan XU, "Lightweight and Fast Low-Light Image Enhancement Method Based on PoolFormer" in IEICE TRANSACTIONS on Information,
vol. E107-D, no. 1, pp. 157-160, January 2024, doi: 10.1587/transinf.2023EDL8051.
Abstract: Images captured in low-light environments have low visibility and high noise, which will seriously affect subsequent visual tasks such as target detection and face recognition. Therefore, low-light image enhancement is of great significance in obtaining high-quality images and is a challenging problem in computer vision tasks. A low-light enhancement model, LLFormer, based on the Vision Transformer, uses axis-based multi-head self-attention and a cross-layer attention fusion mechanism to reduce the complexity and achieve feature extraction. This algorithm can enhance images well. However, the calculation of the attention mechanism is complex and the number of parameters is large, which limits the application of the model in practice. In response to this problem, a lightweight module, PoolFormer, is used to replace the attention module with spatial pooling, which can increase the parallelism of the network and greatly reduce the number of model parameters. To suppress image noise and improve visual effects, a new loss function is constructed for model optimization. The experiment results show that the proposed method not only reduces the number of parameters by 49%, but also performs better in terms of image detail restoration and noise suppression compared with the baseline model. On the LOL dataset, the PSNR and SSIM were 24.098dB and 0.8575 respectively. On the MIT-Adobe FiveK dataset, the PSNR and SSIM were 27.060dB and 0.9490. The evaluation results on the two datasets are better than the current mainstream low-light enhancement algorithms.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDL8051/_p
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@ARTICLE{e107-d_1_157,
author={Xin HU, Jinhua WANG, Sunhan XU, },
journal={IEICE TRANSACTIONS on Information},
title={Lightweight and Fast Low-Light Image Enhancement Method Based on PoolFormer},
year={2024},
volume={E107-D},
number={1},
pages={157-160},
abstract={Images captured in low-light environments have low visibility and high noise, which will seriously affect subsequent visual tasks such as target detection and face recognition. Therefore, low-light image enhancement is of great significance in obtaining high-quality images and is a challenging problem in computer vision tasks. A low-light enhancement model, LLFormer, based on the Vision Transformer, uses axis-based multi-head self-attention and a cross-layer attention fusion mechanism to reduce the complexity and achieve feature extraction. This algorithm can enhance images well. However, the calculation of the attention mechanism is complex and the number of parameters is large, which limits the application of the model in practice. In response to this problem, a lightweight module, PoolFormer, is used to replace the attention module with spatial pooling, which can increase the parallelism of the network and greatly reduce the number of model parameters. To suppress image noise and improve visual effects, a new loss function is constructed for model optimization. The experiment results show that the proposed method not only reduces the number of parameters by 49%, but also performs better in terms of image detail restoration and noise suppression compared with the baseline model. On the LOL dataset, the PSNR and SSIM were 24.098dB and 0.8575 respectively. On the MIT-Adobe FiveK dataset, the PSNR and SSIM were 27.060dB and 0.9490. The evaluation results on the two datasets are better than the current mainstream low-light enhancement algorithms.},
keywords={},
doi={10.1587/transinf.2023EDL8051},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - Lightweight and Fast Low-Light Image Enhancement Method Based on PoolFormer
T2 - IEICE TRANSACTIONS on Information
SP - 157
EP - 160
AU - Xin HU
AU - Jinhua WANG
AU - Sunhan XU
PY - 2024
DO - 10.1587/transinf.2023EDL8051
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E107-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2024
AB - Images captured in low-light environments have low visibility and high noise, which will seriously affect subsequent visual tasks such as target detection and face recognition. Therefore, low-light image enhancement is of great significance in obtaining high-quality images and is a challenging problem in computer vision tasks. A low-light enhancement model, LLFormer, based on the Vision Transformer, uses axis-based multi-head self-attention and a cross-layer attention fusion mechanism to reduce the complexity and achieve feature extraction. This algorithm can enhance images well. However, the calculation of the attention mechanism is complex and the number of parameters is large, which limits the application of the model in practice. In response to this problem, a lightweight module, PoolFormer, is used to replace the attention module with spatial pooling, which can increase the parallelism of the network and greatly reduce the number of model parameters. To suppress image noise and improve visual effects, a new loss function is constructed for model optimization. The experiment results show that the proposed method not only reduces the number of parameters by 49%, but also performs better in terms of image detail restoration and noise suppression compared with the baseline model. On the LOL dataset, the PSNR and SSIM were 24.098dB and 0.8575 respectively. On the MIT-Adobe FiveK dataset, the PSNR and SSIM were 27.060dB and 0.9490. The evaluation results on the two datasets are better than the current mainstream low-light enhancement algorithms.
ER -
English
