An Efficient and Low-Complexity Transformer-Based Deep Learning Framework for High-Dynamic-Range Image Reconstruction
High-dynamic-range (HDR) image reconstruction involves creating an HDR image from multiple low-dynamic-range images as input, providing a computational solution to enhance image quality. This task presents several challenges, such as frame misalignment, overexposure, and motion, which are addressed...
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MDPI AG
2025-02-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/5/1497 |
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| author | Josue Lopez-Cabrejos Thuanne Paixão Ana Beatriz Alvarez Diodomiro Baldomero Luque |
| author_facet | Josue Lopez-Cabrejos Thuanne Paixão Ana Beatriz Alvarez Diodomiro Baldomero Luque |
| author_sort | Josue Lopez-Cabrejos |
| collection | DOAJ |
| description | High-dynamic-range (HDR) image reconstruction involves creating an HDR image from multiple low-dynamic-range images as input, providing a computational solution to enhance image quality. This task presents several challenges, such as frame misalignment, overexposure, and motion, which are addressed using deep learning algorithms. In this context, various architectures with different approaches exist, such as convolutional neural networks, diffusion networks, generative adversarial networks, and Transformer-based architectures, with the latter offering the best quality but at a high computational cost. This paper proposes an HDR reconstruction architecture using a Transformer-based approach to achieve results competitive with the state of the art while reducing computational cost. The number of self-attention blocks was reduced for feature refinement. To prevent quality degradation, a Convolutional Block Attention Module was added, enhancing image features by using the central frame as a reference. The proposed architecture was evaluated on two datasets, achieving the best results on Tel’s dataset in terms of quality metrics. The computational cost indicated that the architecture was significantly more efficient than other Transformer-based approaches for reconstruction. The results of this research suggest that low-complexity Transformer-based architectures have great potential, with applications extending beyond HDR reconstruction to other domains. |
| format | Article |
| id | doaj-art-b8c4adaa7c694230aa0d171fc9766cd1 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-b8c4adaa7c694230aa0d171fc9766cd12025-08-20T02:52:38ZengMDPI AGSensors1424-82202025-02-01255149710.3390/s25051497An Efficient and Low-Complexity Transformer-Based Deep Learning Framework for High-Dynamic-Range Image ReconstructionJosue Lopez-Cabrejos0Thuanne Paixão1Ana Beatriz Alvarez2Diodomiro Baldomero Luque3PAVIC Laboratory, University of Acre (UFAC), Rio Branco 69915-900, BrazilPAVIC Laboratory, University of Acre (UFAC), Rio Branco 69915-900, BrazilPAVIC Laboratory, University of Acre (UFAC), Rio Branco 69915-900, BrazilPAVIC Laboratory, University of Acre (UFAC), Rio Branco 69915-900, BrazilHigh-dynamic-range (HDR) image reconstruction involves creating an HDR image from multiple low-dynamic-range images as input, providing a computational solution to enhance image quality. This task presents several challenges, such as frame misalignment, overexposure, and motion, which are addressed using deep learning algorithms. In this context, various architectures with different approaches exist, such as convolutional neural networks, diffusion networks, generative adversarial networks, and Transformer-based architectures, with the latter offering the best quality but at a high computational cost. This paper proposes an HDR reconstruction architecture using a Transformer-based approach to achieve results competitive with the state of the art while reducing computational cost. The number of self-attention blocks was reduced for feature refinement. To prevent quality degradation, a Convolutional Block Attention Module was added, enhancing image features by using the central frame as a reference. The proposed architecture was evaluated on two datasets, achieving the best results on Tel’s dataset in terms of quality metrics. The computational cost indicated that the architecture was significantly more efficient than other Transformer-based approaches for reconstruction. The results of this research suggest that low-complexity Transformer-based architectures have great potential, with applications extending beyond HDR reconstruction to other domains.https://www.mdpi.com/1424-8220/25/5/1497high-dynamic-range imagingimage reconstructionchannel attentionspatial attentionTransformers |
| spellingShingle | Josue Lopez-Cabrejos Thuanne Paixão Ana Beatriz Alvarez Diodomiro Baldomero Luque An Efficient and Low-Complexity Transformer-Based Deep Learning Framework for High-Dynamic-Range Image Reconstruction Sensors high-dynamic-range imaging image reconstruction channel attention spatial attention Transformers |
| title | An Efficient and Low-Complexity Transformer-Based Deep Learning Framework for High-Dynamic-Range Image Reconstruction |
| title_full | An Efficient and Low-Complexity Transformer-Based Deep Learning Framework for High-Dynamic-Range Image Reconstruction |
| title_fullStr | An Efficient and Low-Complexity Transformer-Based Deep Learning Framework for High-Dynamic-Range Image Reconstruction |
| title_full_unstemmed | An Efficient and Low-Complexity Transformer-Based Deep Learning Framework for High-Dynamic-Range Image Reconstruction |
| title_short | An Efficient and Low-Complexity Transformer-Based Deep Learning Framework for High-Dynamic-Range Image Reconstruction |
| title_sort | efficient and low complexity transformer based deep learning framework for high dynamic range image reconstruction |
| topic | high-dynamic-range imaging image reconstruction channel attention spatial attention Transformers |
| url | https://www.mdpi.com/1424-8220/25/5/1497 |
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