U-Know-DiffPAN：一种用于全色锐化的具备细节增强能力的不确定性感知知识蒸馏扩散框架
U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening
AI总结：
针对传统全色锐化细节恢复差、扩散法输入利用不充分的问题，提出U-Know-DiffPAN框架，通过不确定性感知知识蒸馏结合频率注意力与小波变换，实现轻量模型的高性能全色锐化，效果优于现有前沿方法。
摘要：
传统全色锐化方法因高频信息利用能力有限，往往难以恢复精细细节。此外，基于扩散的方法缺乏足够的条件约束，无法有效充分利用全色（Panchromatic，PAN）图像和低分辨率多光谱（low-resolution multispectral，LRMS）输入。为解决这些挑战，我们提出一种用于全色锐化的具备细节增强能力的不确定性感知知识蒸馏扩散框架，命名为U-Know-DiffPAN。\nU-Know-DiffPAN引入不确定性感知知识蒸馏，将教师模型的特征细节有效迁移至学生模型。该框架的教师模型通过频率选择注意力捕捉频率细节，助力准确的逆过程学习。我们通过将编码器以PAN和LRMS的紧凑向量表示为条件、将解码器以小波变换为条件，实现丰富的频率利用。由此，高容量教师模型在不确定性图的辅助下，将富含频率信息的特征蒸馏到轻量级学生模型中；教师模型可借助不确定性图引导学生模型聚焦全色锐化中的困难图像区域。\n在多类数据集上开展的大量实验表明，U-Know-DiffPAN相较于最新的前沿全色锐化方法，具备更强的鲁棒性和更优的性能。
英文摘要：
Conventional methods for PAN-sharpening often struggle to restore fine details due to limitations in leveraging high-frequency information. Moreover, diffusion-based approaches lack sufficient conditioning to fully utilize Panchromatic (PAN) images and low-resolution multispectral (LRMS) inputs effectively. To address these challenges, we propose an uncertainty-aware knowledge distillation diffusion framework with details enhancement for PAN-sharpening, called U-Know-DiffPAN. The U-Know-DiffPAN incorporates uncertainty-aware knowledge distillation for effective transfer of feature details from our teacher model to a student one. The teacher model in our U-Know-DiffPAN captures frequency details through freqeuncy selective attention, facilitating accurate reverse process learning. By conditioning the encoder on compact vector representations of PAN and LRMS and the decoder on Wavelet transforms, we enable rich frequency utilization. So, the high-capacity teacher model distills frequency-rich features into a lightweight student model aided by an uncertainty map. From this, the teacher model can guide the student model to focus on difficult image regions for PAN-sharpening via the usage of the uncertainty map. Extensive experiments on diverse datasets demonstrate the robustness and superior performance of our U-Know-DiffPAN over very recent state-of-the-art PAN-sharpening methods.
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