Diffusion Probabilistic Models for Compressive SAR Imaging
On the map: Diffusion models → Inverse problems and image restoration
Abstract (arXiv)
Compressed sensing Synthetic Aperture Radar (SAR) image formation, formulated as an inverse problem and solved with traditional iterative optimization methods can be very computationally expensive. We investigate the use of denoising diffusion probabilistic models for compressive SAR image reconstruction, where the diffusion model is guided by a poor initial reconstruction from sub-sampled data obtained via standard imaging methods. We present results on real SAR data and compare our compressively sampled diffusion model reconstruction with standard image reconstruction methods utilizing the full data set, demonstrating the potential performance gains in imaging quality.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 9 of its 18 references that are in the map. First posted on arXiv 23 Apr 2025.
Builds on
9 of 18 references.
Closest ancestors · 2
Same direction, newest first
- 2024SAR Image Synthesis with Diffusion Models13 May
- 2022Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models29 Jul
Other references · 7
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
- 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
- 2022Diffusion Models in Vision: A Surveycited by 1,015
- 2022DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstructioncited by 74
- 2021Sparse regularization with a non-convex penalty for SAR imaging and autofocusingcited by 1
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026Diffusion Meets Unrolling: Compressive SAR Image Reconstruction with Interleaved Learned Corrections
- 2025Diffusion-Prior Split Gibbs Sampling for Synthetic Aperture Radar Imaging under Incomplete Measurements
- 2024Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Models
- 2023SDDPM: Speckle Denoising Diffusion Probabilistic Models
- 2024SAR Despeckling via Regional Denoising Diffusion Probabilistic Model
- 2025Zero-Shot Solving of Imaging Inverse Problems via Noise-Refined Likelihood Guided Diffusion Models
- 2024Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors
- 2022SAR Despeckling using a Denoising Diffusion Probabilistic Model