Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908688991649792 |
|---|---|
| author | Cai, Zhongnan Wang, Yingying Zheng, Hui Pan, Panwang Lin, ZiXu Meng, Ge Li, Chenxin He, Chunming Xie, Jiaxin Lin, Yunlong Lu, Junbin Huang, Yue Ding, Xinghao |
| author_facet | Cai, Zhongnan Wang, Yingying Zheng, Hui Pan, Panwang Lin, ZiXu Meng, Ge Li, Chenxin He, Chunming Xie, Jiaxin Lin, Yunlong Lu, Junbin Huang, Yue Ding, Xinghao |
| contents | Recently, deep learning-based pan-sharpening algorithms have achieved notable advancements over traditional methods. However, deep learning-based methods incur substantial computational overhead during inference, especially with large images. This excessive computational demand limits the applicability of these methods in real-world scenarios, particularly in the absence of dedicated computing devices such as GPUs and TPUs. To address these challenges, we propose Pan-LUT, a novel learnable look-up table (LUT) framework for pan-sharpening that strikes a balance between performance and computational efficiency for large remote sensing images. Our method makes it possible to process 15K*15K remote sensing images on a 24GB GPU. To finely control the spectral transformation, we devise the PAN-guided look-up table (PGLUT) for channel-wise spectral mapping. To effectively capture fine-grained spatial details, we introduce the spatial details look-up table (SDLUT). Furthermore, to adaptively aggregate channel information for generating high-resolution multispectral images, we design an adaptive output look-up table (AOLUT). Our model contains fewer than 700K parameters and processes a 9K*9K image in under 1 ms using one RTX 2080 Ti GPU, demonstrating significantly faster performance compared to other methods. Experiments reveal that Pan-LUT efficiently processes large remote sensing images in a lightweight manner, bridging the gap to real-world applications. Furthermore, our model surpasses SOTA methods in full-resolution scenes under real-world conditions, highlighting its effectiveness and efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23793 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables Cai, Zhongnan Wang, Yingying Zheng, Hui Pan, Panwang Lin, ZiXu Meng, Ge Li, Chenxin He, Chunming Xie, Jiaxin Lin, Yunlong Lu, Junbin Huang, Yue Ding, Xinghao Computer Vision and Pattern Recognition Recently, deep learning-based pan-sharpening algorithms have achieved notable advancements over traditional methods. However, deep learning-based methods incur substantial computational overhead during inference, especially with large images. This excessive computational demand limits the applicability of these methods in real-world scenarios, particularly in the absence of dedicated computing devices such as GPUs and TPUs. To address these challenges, we propose Pan-LUT, a novel learnable look-up table (LUT) framework for pan-sharpening that strikes a balance between performance and computational efficiency for large remote sensing images. Our method makes it possible to process 15K*15K remote sensing images on a 24GB GPU. To finely control the spectral transformation, we devise the PAN-guided look-up table (PGLUT) for channel-wise spectral mapping. To effectively capture fine-grained spatial details, we introduce the spatial details look-up table (SDLUT). Furthermore, to adaptively aggregate channel information for generating high-resolution multispectral images, we design an adaptive output look-up table (AOLUT). Our model contains fewer than 700K parameters and processes a 9K*9K image in under 1 ms using one RTX 2080 Ti GPU, demonstrating significantly faster performance compared to other methods. Experiments reveal that Pan-LUT efficiently processes large remote sensing images in a lightweight manner, bridging the gap to real-world applications. Furthermore, our model surpasses SOTA methods in full-resolution scenes under real-world conditions, highlighting its effectiveness and efficiency. |
| title | Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.23793 |