arXiv Computer Vision
A Tunable Despeckling Neural Network Stabilized via Diffusion Equation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Sep 15
SAR-FAH: A Frequency-Adaptive Hybrid Network based on Neural ODEs for Structural-Preserving SAR Despeckling
SAR-FAH is a Frequency‑Adaptive Hybrid network that uses Neural Ordinary Differential Equations (NODEs) to despeckle Synthetic Aperture Radar (SAR) images. It separates homogeneous and heterogeneous regions in the frequency domain via wavelet transform, then applies a NODE‑based module to low‑frequency sub‑bands for smooth denoising and an enhanced U‑Net with deformable convolutions to high‑frequency sub‑bands for edge and texture preservation. Experiments on synthetic and real SAR data show that SAR‑FAH outperforms current state‑of‑the‑art despeckling methods both quantitatively and qualitatively.
arXiv Machine Learning
Oct 28
A JiT-Based Denoising Defense Method Against Adversarial Examples for Radio Signal Automatic Modulation Classification
The paper introduces a JiT-based denoising defense for radio signal automatic modulation classification, employing a 1D Transformer trained via flow matching to map adversarial examples onto a clean signal manifold. Experiments on the RML2018.01A dataset show the method outperforms PGD-based adversarial training, improving robust accuracy by at least 20.58% under PGD attacks and clean accuracy by 10.20%. It also demonstrates stronger defense against unseen attack types, achieving up to a 39.72% improvement in robust accuracy at higher attack strengths.
arXiv Machine Learning
Jun 16
Training-Free Adversarial Robustness in Computational MRI
arXiv:2501. 01908v4 Announce Type: replace-cross Abstract: Deep learning (DL) methods have become the state-of-the-art for reconstructing sub-sampled magnetic resonance imaging (MRI) data.
arXiv Computer Vision
Sep 2
C-DiffSET: Leveraging Latent Diffusion for SAR-to-EO Image Translation with Confidence-Guided Reliable Object Generation
C‑DiffSET is a SAR‑to‑EO image translation framework that uses a pretrained Latent Diffusion Model to adapt SAR imagery to the EO domain. The method exploits the pretrained VAE encoder’s ability to map SAR and EO images into a shared latent space, even when SAR inputs contain varying noise levels. A confidence‑guided diffusion loss further improves pixel‑wise fidelity by reducing artifacts such as appearing or disappearing objects, leading to state‑of‑the‑art results across multiple datasets.
arXiv Machine Learning
Sep 15
Noise-Aware and Dynamically Adaptive Federated Defense Framework for SAR Image Target Recognition
arXiv:2601.00900v2 Announce Type: replace-cross Abstract: As a critical application of computational intelligence in remote sensing, deep learning-based synthetic aperture radar (SAR) image target re...
arXiv AI
Jul 31
PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for Low-dose CT imaging
arXiv:2602. 21987v3 Announce Type: replace-cross Abstract: Low-dose CT images are essential for reducing radiation exposure in cancer screening, pediatric imaging, and longitudinal monitoring protocols, but their quality is often degraded by noise from low-dose acquisition, patient motion, or scanner limitations, affecting both clinical interpretation and downstream analysis.