Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables

• URL: http://arxiv.org/abs/2503.23793v1
• Date: Mon, 31 Mar 2025 07:13:59 GMT
• Title: Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables
• Authors: Zhongnan Cai, Yingying Wang, Yunlong Lin, Hui Zheng, Ge Meng, Zixu Lin, Jiaxin Xie, Junbin Lu, Yue Huang, Xinghao Ding,
• Abstract summary: We propose Pan-LUT, a learnable look-up table framework for pan-sharpening.<n>Pan-LUT balances performance and computational efficiency for high-resolution remote sensing images.<n>Our proposed method contains fewer than 300K parameters and processes a 8K resolution image in under 1 ms.
• Score: 32.23794092167474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, deep learning-based pan-sharpening algorithms have achieved notable advancements over traditional methods. However, many deep learning-based approaches incur substantial computational overhead during inference, especially with high-resolution 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 high-resolution remote sensing images. 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 and adaptively learn local contexts, we introduce the spatial details look-up table (SDLUT) and adaptive aggregation look-up table (AALUT). Our proposed method contains fewer than 300K parameters and processes a 8K resolution image in under 1 ms using a single NVIDIA GeForce 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.

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