A Tunable Despeckling Neural Network Stabilized via Diffusion Equation

• URL: http://arxiv.org/abs/2411.15921v1
• Date: Sun, 24 Nov 2024 17:08:43 GMT
• Title: A Tunable Despeckling Neural Network Stabilized via Diffusion Equation
• Authors: Yi Ran, Zhichang Guo, Jia Li, Yao Li, Martin Burger, Boying Wu,
• Abstract summary: Multiplicative Gamma noise remove is a critical research area in the application of synthetic aperture radar (SAR) imaging. We propose a tunable, regularized neural network that unrolls a denoising unit and a regularization unit into a single network for end-to-end training.
• Score: 15.996302571895045
• License:
• Abstract: Multiplicative Gamma noise remove 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 work by finding perturbations that significantly disrupt functionality of neural networks, as the inherent instability of neural networks makes them highly susceptible. A network designed to withstand such extreme cases can more effectively mitigate general disturbances in real SAR data. In this work, the dissipative nature of diffusion equations is employed to underpin a novel approach for countering adversarial attacks and improve the resistance of real noise disturbance. We propose a tunable, regularized neural network that unrolls a denoising unit and a regularization unit into a single network for end-to-end training. In the network, the denoising unit and the regularization unit are composed of the denoising network and the simplest linear diffusion equation respectively. The regularization unit enhances network stability, allowing post-training time step adjustments to effectively mitigate the adverse impacts of adversarial attacks. The stability and convergence of our model are theoretically proven, and in the experiments, we compare our model with several state-of-the-art denoising methods on simulated images, adversarial samples, and real SAR images, yielding superior results in both quantitative and visual evaluations.

Related papers

• Beyond Pruning Criteria: The Dominant Role of Fine-Tuning and Adaptive Ratios in Neural Network Robustness [7.742297876120561]
  Deep neural networks (DNNs) excel in tasks like image recognition and natural language processing. Traditional pruning methods compromise the network's ability to withstand subtle perturbations. This paper challenges the conventional emphasis on weight importance scoring as the primary determinant of a pruned network's performance.
  arXiv  Detail & Related papers  (2024-10-19T18:35:52Z)
• Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning [24.63284452991301]
  We propose a doubly robust causal effect estimator under networked interference. Specifically, we generalize the targeted learning technique into the networked interference setting. We devise an end-to-end causal effect estimator by transforming the identified theoretical condition into a targeted loss.
  arXiv  Detail & Related papers  (2024-05-06T10:49:51Z)
• Defending Spiking Neural Networks against Adversarial Attacks through Image Purification [20.492531851480784]
  Spiking Neural Networks (SNNs) aim to bridge the gap between neuroscience and machine learning. SNNs are vulnerable to adversarial attacks like convolutional neural networks. We propose a biologically inspired methodology to enhance the robustness of SNNs.
  arXiv  Detail & Related papers  (2024-04-26T00:57:06Z)
• Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack [60.40356882897116]
  Deep neural networks (DNNs) have shown superior performance compared to traditional image denoising algorithms. In this paper, we propose an adversarial attack method named denoising-PGD which can successfully attack all the current deep denoising models.
  arXiv  Detail & Related papers  (2023-06-28T09:30:59Z)
• Momentum Diminishes the Effect of Spectral Bias in Physics-Informed Neural Networks [72.09574528342732]
  Physics-informed neural network (PINN) algorithms have shown promising results in solving a wide range of problems involving partial differential equations (PDEs) They often fail to converge to desirable solutions when the target function contains high-frequency features, due to a phenomenon known as spectral bias. In the present work, we exploit neural tangent kernels (NTKs) to investigate the training dynamics of PINNs evolving under gradient descent with momentum (SGDM)
  arXiv  Detail & Related papers  (2022-06-29T19:03:10Z)
• Real-time Over-the-air Adversarial Perturbations for Digital Communications using Deep Neural Networks [0.0]
  adversarial perturbations can be used by RF communications systems to avoid reactive-jammers and interception systems. This work attempts to bridge this gap by defining class-specific and sample-independent adversarial perturbations. We demonstrate the effectiveness of these attacks over-the-air across a physical channel using software-defined radios.
  arXiv  Detail & Related papers  (2022-02-20T14:50:52Z)
• Non-Singular Adversarial Robustness of Neural Networks [58.731070632586594]
  Adrial robustness has become an emerging challenge for neural network owing to its over-sensitivity to small input perturbations. We formalize the notion of non-singular adversarial robustness for neural networks through the lens of joint perturbations to data inputs as well as model weights.
  arXiv  Detail & Related papers  (2021-02-23T20:59:30Z)
• Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
  Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
  arXiv  Detail & Related papers  (2020-12-17T15:19:29Z)
• Operational vs Convolutional Neural Networks for Image Denoising [25.838282412957675]
  Convolutional Neural Networks (CNNs) have recently become a favored technique for image denoising due to its adaptive learning ability. We propose a heterogeneous network model which allows greater flexibility for embedding additional non-linearity at the core of the data transformation. An extensive set of comparative evaluations of ONNs and CNNs over two severe image denoising problems yield conclusive evidence that ONNs enriched by non-linear operators can achieve a superior denoising performance against CNNs with both equivalent and well-known deep configurations.
  arXiv  Detail & Related papers  (2020-09-01T12:15:28Z)
• Network Diffusions via Neural Mean-Field Dynamics [52.091487866968286]
  We propose a novel learning framework for inference and estimation problems of diffusion on networks. Our framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node infection probabilities. Our approach is versatile and robust to variations of the underlying diffusion network models.
  arXiv  Detail & Related papers  (2020-06-16T18:45:20Z)
• Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness [97.67477497115163]
  We use mode connectivity to study the adversarial robustness of deep neural networks. Our experiments cover various types of adversarial attacks applied to different network architectures and datasets. Our results suggest that mode connectivity offers a holistic tool and practical means for evaluating and improving adversarial robustness.
  arXiv  Detail & Related papers  (2020-04-30T19:12:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.