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<title>U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening</title>
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<img src="./static/image/globe.png" width="40" height="40">
<font color="#d91421"><b>U</b></font>-
<font color="#008000"><b>Know</b></font>-
<font color="#00BFFF"><b>Diff</b></font>-<b>PAN</b>:
<font color="#d91421"><b>U</b></font>ncertainty-aware
<font color="#008000"><b>Know</b></font>ledge Distillation
<font color="#00BFFF"><b>Diff</b></font>usion Framework with Details Enhancement for <b>PAN</b>-Sharpening
</h1>
</div> <div class="is-size-5 publication-authors">
<span class="author-block">
<a target="_blank" rel="noopener noreferrer" href="https://www.viclab.kaist.ac.kr/">Sungpyo Kim</a><sup> 1</sup>
</span>
<span class="author-block">
<a target="_blank" rel="noopener noreferrer" href="https://sites.google.com/view/jeonghyeokdo">Jeonghyeok Do</a><sup> 1</sup>
</span>
<span class="author-block">
<a target="_blank" rel="noopener noreferrer" href="https://sites.google.com/view/knuairlab/">Jaehyup Lee</a><sup>† 2</sup>
</span>
<span class="author-block">
<a target="_blank" rel="noopener noreferrer" href="https://scholar.google.com/citations?hl=ko&user=bGXte_4AAAAJ">Munchurl Kim</a><sup>† 1</sup>
</span>
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<span class="author-block"> <sup>†</sup>Co-corresponding authors</span>
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<span class="author-block"><sup>1</sup>Korea Advanced Institute of Science and Technology, South Korea</span>
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<span class="author-block"><sup>2</sup> Kyungpook National University, South Korea</span>
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<!-- <p>
We present a joint learning scheme of video super-resolution and deblurring, called VSRDB, to restore clean high-resolution (HR) videos from blurry low-resolution (LR) ones.
This joint restoration problem has drawn much less attention compared to single restoration problems.
</p>
<p>
We propose a novel flow-guided dynamic filtering (FGDF) and iterative feature refinement with multi-attention (FRMA), which constitutes our VSRDB framework, denoted as FMA-Net.
Specifically, our proposed FGDF enables precise estimation of both spatio-temporally-variant degradation and restoration kernels that are aware of motion trajectories through sophisticated motion representation learning.
Compared to conventional dynamic filtering, the FGDF enables the FMA-Net to effectively handle large motions into the VSRDB.
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<p>
Additionally, the stacked FRMA blocks trained with our novel temporal anchor (TA) loss, which temporally anchors and sharpens features, refine features in a course-to-fine manner through iterative updates.
Extensive experiments demonstrate the superiority of the proposed FMA-Net over state-of-the-art methods in terms of both quantitative and qualitative quality.
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<p>
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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The overall pipeline of U-Know-DiffPAN for PAN-sharpening. Our framework operates in two main stages: (i) pre-training the FSA-T within a diffusion process to produce an initial prediction alongside
an uncertainty map that identifies spatially weak regions; and (ii) training the FSA-S by leveraging this uncertainty map to guide the FSA-S in refining these regions through the KD.
</p>
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<!-- <b>Top:</b> The overall pipeline of U-Know-DiffPAN for PAN-sharpening. <br>
<b>Bottom:</b> (a) Architecture of FSA-T block; (b) Architecture of FSA-S block. -->
Architecture of FSA-T block
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<h2 class="title is-3">Quantitative Results</h2>
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<p>
Quantitative comparison on GF2 dataset.
Comparison of different models on the GaoFen-2 (GF2) dataset. Blue indicates the second-best performance, while Red highlights the best-performing model.
</p>
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<image src="./static/image/WV3_QB_table.png" class="img-responsive" alt="DDNeRF_Architecture_v21"><br>
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<p>
Quantitative comparison on WV3 and QB dataset.
Comparison of different models on the WorldView-3 (WV3) and QuickBird (QB) datasets. Blue indicates the second-best performance, while Red highlights the best-performing model.
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<h2 class="title is-3">Qualitative Results</h2>
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<image src="./static/image/suppl_reduced_gf2.png" class="img-responsive" alt="DDNeRF_Architecture_v21"><br>
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<p>
PAN-sharpening results for the GF2 dataset under reduced resolution (RR) scenarios. The first row depicts the output HRMS
images, while the second row highlights the Error Map between the output HRMS and the corresponding ground truth images.The Mean
Absolute Error (MAE) values are presented alongside the Error Map.
</p>
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<image src="./static/image/suppl_reduced_qb.png" class="img-responsive" alt="DDNeRF_Architecture_v21"><br>
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<p>
PAN-sharpening results for the QB dataset under reduced resolution (RR) scenarios. The first row depicts the output HRMS
images, while the second row highlights the Error Map between the output HRMS and the corresponding ground truth images. The Mean
Absolute Error (MAE) values are presented alongside the Error Map.
</p>
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<image src="./static/image/suppl_reduced_wv3.png" class="img-responsive" alt="DDNeRF_Architecture_v21"><br>
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<p>
PAN-sharpening results for the GF2 dataset under reduced resolution (RR) scenarios. The first row depicts the output HRMS
images, while the second row highlights the Error Map between the output HRMS and the corresponding ground truth images. The Mean
Absolute Error (MAE) values are presented alongside the Error Map.
</p>
</div>
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<image src="./static/image/suppl_full_wv3.png" class="img-responsive" alt="DDNeRF_Architecture_v21"><br>
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<p>
PAN-sharpening results for the WV3 dataset under full resolution (FR) scenarios. The first row depicts the output HRMS
images.
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<image src="./static/image/suppl_full_qb.png" class="img-responsive" alt="DDNeRF_Architecture_v21"><br>
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<p>
PAN-sharpening results for the WV3 dataset under full resolution (FR) scenarios. The first row depicts the output HRMS
images.
</p>
</div>
</div>
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<image src="./static/image/suppl_full_gf2.png" class="img-responsive" alt="DDNeRF_Architecture_v21"><br>
<div class="content has-text-justified">
<p>
PAN-sharpening results for the GF2 dataset under full resolution (FR) scenarios. The first row depicts the output HRMS images.
</p>
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<h2 class="title is-3">Acknowledgement</h2>
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This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT): No. 2021-0-00087, Development of high-quality conversion technology for SD/HD low-quality media and No. RS2022-00144444, Deep Learning Based Visual Representational Learning and Rendering of Static and Dynamic Scenes.
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<h2 class="title">BibTeX</h2>
<pre><code>@InProceedings{Youk_2024_CVPR,
author = {Kim, Sungpyo and Kim, Sungpyo and Kim, Munchurl},
title = {U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2024},
pages = {44-55}
}</code></pre>
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