A data-driven algorithm for solving image despeckling PDE model using physics-informed ConvNet

In this study, we propose a new data-driven algorithm for the Perona-Malik image despeckling problem. The advantage of the proposed algorithm over neural network-based methods is that it does not need any noisy and clean image data pair for training. The proposed algorithm is implemented using a thr...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Computers & mathematics with applications (1987) Ročník 200; s. 202 - 227
Hlavní autoři: Kumar, Haridarshan, Kumar, Sanjeev
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.12.2025
Témata:
Convolutional neural networks
Diffusion
Finite difference method
Image despeckling
PDEs
Physics-informed machine learning
Physics-informed machine learning
Convolutional neural networks
Diffusion
Image despeckling
PDEs
Finite difference method
ISSN:0898-1221
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In this study, we propose a new data-driven algorithm for the Perona-Malik image despeckling problem. The advantage of the proposed algorithm over neural network-based methods is that it does not need any noisy and clean image data pair for training. The proposed algorithm is implemented using a three-dimensional convolution neural network (ConvNet) architecture. We compare its output with results obtained from several existing methods, including the operator splitting RBF collocation method, the finite difference method (FDM), and physics-informed neural networks (PINNs). To evaluate the performance of the proposed algorithm, simulations are carried out using grayscale images that have been artificially corrupted with different levels of speckle noise. Using the peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) as the evaluation metric, we observed that the proposed algorithm outperforms these existing methods, demonstrating superior image quality with the same numerical scheme and the same discretization. To the best of our knowledge, this work represents the first application of physics-inspired convolutional neural network for PDE-based image despeckling model.
ISSN:0898-1221
DOI:10.1016/j.camwa.2025.09.022