KAO: Kernel-Adaptive Optimization

Teerapong Panboonyuen
Chulalongkorn University

🎉 Accepted to IEEE Transactions on Geoscience and Remote Sensing
(TGRS, Impact Factor: 8.6)
Teaser
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Abstract

Satellite image inpainting is critical in remote sensing applications. We propose KAO, a diffusion-based framework enhanced with Kernel-Adaptive Optimization and Token Pyramid Transformer (TPT), enabling dynamic kernel modulation in latent space. KAO delivers high-fidelity, structure-aware reconstructions, outperforming existing models like Stable Diffusion, RePaint, and SatDiff across VHR datasets.

Results Overview

Qualitative comparison with 7 models. KAO shows superior restoration across various occlusion patterns.
Detailed sample comparisons. KAO excels in reconstructing linear features and textures in urban scenes.

How to Read the Following Results:
Each scene below presents a qualitative comparison of inpainting performance across seven models. From left to right, the columns show: (1) the occluded input, (2) the ground truth image, followed by the outputs of (3) Stable Diffusion [25], (4) RePaint [16], (5) SatDiff [1], (6) DPS [26], (7) PSLD [27], and (8) our method – KAO. Each row corresponds to a different scene type—ranging from urban to agricultural landscapes and cloud-covered areas. Compare across columns to evaluate each model’s ability to restore structural details and textures. KAO consistently produces high-fidelity outputs that preserve spatial layout, align with real-world features, and outperform others in restoring occluded regions.

Scene 1 – Urban satellite reconstruction comparison.
Scene 2 – Agricultural patterns, occlusion restoration.
Scene 3 – Reconstruction under heavy cloud occlusions.
Scene 4 – Comparison on semi-urban environment.
Scene 5 – Multi-resolution image restoration results.
Scene 6 – Zoomed-in structural fidelity of KAO.

BibTeX Citation

@article{panboonyuen2025kao,
      author    = {Teerapong Panboonyuen},
      title     = {KAO: Kernel-Adaptive Optimization in Diffusion for Satellite Image},
      journal   = {IEEE Transactions on Geoscience and Remote Sensing},
      year      = {2025},
      doi       = {10.1109/TGRS.2025.3621738},
      note      = {Manuscript No. TGRS-2025-06970},
      publisher = {IEEE}
    }