A Tunable Despeckling Neural Network Stabilized via Diffusion Equation

arXiv:2411.15921 · cs.CV, eess.IV · Submitted 2024-11-24 · Read on arXiv

cs.CV, eess.IV

Submitted: 2024-11-24

Updated: 2026-09-21

Code: https://github.com/AmberHen/WHU-OPT-SAR-dataset

Terminology

• Adversarial Attacks — 29× in this paper · explained in 1 episode
• Robustness — 26× in this paper · explained in 5 episodes
• Regularization — 11× in this paper · explained in 3 episodes
• Adaptability — 6× in this paper · explained in 1 episode
• Aperture — 5× in this paper · explained in 1 episode
• Adversarial Examples — 3× in this paper · explained in 2 episodes
• Adversarial Training — 3× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Transferability — 3× in this paper · explained in 4 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Loss Function — 1× in this paper · explained in 2 episodes

Sources

• Explaining and Harnessing Adversarial Examples
• Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack
• Towards Adversarially Robust Deep Image Denoising
• Lipschitz regularized Deep Neural Networks generalize and are adversarially robust
• Intriguing properties of neural networks
• FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

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