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| 001 | 644964 | ||
| 005 | 20260202210343.0 | ||
| 024 | 7 | _ | |a 10.1016/j.sigpro.2025.110324 |2 doi |
| 024 | 7 | _ | |a 0165-1684 |2 ISSN |
| 024 | 7 | _ | |a 1872-7557 |2 ISSN |
| 037 | _ | _ | |a PUBDB-2026-00507 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 000 |
| 100 | 1 | _ | |a Ran, Yi |b 0 |
| 245 | _ | _ | |a A tunable despeckling neural network stabilized via diffusion equation |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2026 |b Elsevier |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1770024283_3939692 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a the German Research Foundation, Germany with grant BU 2327/20-1 |
| 520 | _ | _ | |a The removal of multiplicative Gamma noise is a critical research area in the application of synthetic aperture radar (SAR) imaging, where neural networks serve as a potent tool. However, real-world data often diverges from theoretical models, exhibiting various disturbances, which makes the neural network less effective. Adversarial attacks can be used as a criterion for judging the adaptability of neural networks to real data, since they can find the most extreme perturbations that make neural networks ineffective. In this work, we propose a tunable, regularized neural network framework that unrolls a shallow neural denoising block and a diffusion regularization block into a single network for end-to-end training. The linear heat equation, known for its inherent smoothness and low-pass filtering properties, is adopted as the diffusion regularization block. The smoothness of our outputs is controlled by a single time step hyperparameter that can be adjusted dynamically. The stability and convergence of our model are theoretically proven. Experimental results demonstrate that the proposed model effectively eliminates high-frequency oscillations induced by adversarial attacks. Finally, the proposed model is benchmarked against several state-of-the-art denoising methods on simulated images, adversarial samples, and real SAR images, achieving superior performance in both quantitative and visual evaluations. |
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| 700 | 1 | _ | |a Guo, Zhichang |b 1 |
| 700 | 1 | _ | |a Li, Jia |0 P:(DE-HGF)0 |b 2 |e Corresponding author |
| 700 | 1 | _ | |a Li, Yao |0 0000-0002-1754-4528 |b 3 |
| 700 | 1 | _ | |a Burger, Martin |0 P:(DE-H253)PIP1103953 |b 4 |
| 700 | 1 | _ | |a Wu, Boying |b 5 |
| 773 | _ | _ | |a 10.1016/j.sigpro.2025.110324 |g Vol. 239, p. 110324 - |0 PERI:(DE-600)1466346-6 |p 110324 |t Signal processing |v 239 |y 2026 |x 0165-1684 |
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