mlsanityData to 7 Oct 2026

Sim2Real SAR Image Restoration: Metadata-Driven Models for Joint Despeckling and Sidelobes Reduction

arXiv 2601.01541 ↗First posted 4 Jan 2026eess.IV cs.CV eess.SPcited by 0

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Abstract (arXiv)

Synthetic aperture radar (SAR) provides valuable information about the Earth's surface under all weather and illumination conditions. However, the inherent phenomenon of speckle and the presence of sidelobes around bright targets pose challenges for accurate interpretation of SAR imagery. Most existing SAR image restoration methods address despeckling and sidelobes reduction as separate tasks. In this paper, we propose a unified framework that jointly performs both tasks using neural networks (NNs) trained on a realistic SAR simulated dataset generated with MOCEM. Inference can then be performed on real SAR images, demonstrating effective simulation to real (Sim2Real) transferability. Additionally, we incorporate acquisition metadata as auxiliary input to the NNs, demonstrating improved restoration performance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 34 references that are in the map. First posted on arXiv 4 Jan 2026.

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17 of 34 references.

References · 17

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5 more
  1. 2017SAR Image Despeckling Using a Convolutional Neural Networkcited by 24
  2. 2022SAR Despeckling using a Denoising Diffusion Probabilistic Modelcited by 19
  3. 2021As if by magic: self-supervised training of deep despeckling networks with MERLINcited by 18
  4. 2018MSCE: An edge preserving robust loss function for improving super-resolution algorithmscited by 4
  5. 2023Metadata Improves Segmentation Through Multitasking Elicitationcited by 1

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