WIPUNet: A Physics-inspired Network with Weighted Inductive Biases for Image Denoising
This is an incremental proof of concept for improving robustness in image denoising under strong corruption, primarily for researchers in computer vision and physics-inspired AI.
The paper tackled image denoising by embedding physics-inspired inductive biases into neural networks, showing that their WIPUNet model achieves a widening performance margin over standard baselines at higher noise levels on CIFAR-10 and BSD500 datasets.
In high-energy particle physics, collider measurements are contaminated by "pileup", overlapping soft interactions that obscure the hard-scatter signal of interest. Dedicated subtraction strategies exploit physical priors such as conservation, locality, and isolation. Inspired by this analogy, we investigate how such principles can inform image denoising by embedding physics-guided inductive biases into neural architectures. This paper is a proof of concept: rather than targeting state-of-the-art (SOTA) benchmarks, we ask whether physics-inspired priors improve robustness under strong corruption. We introduce a hierarchy of PU-inspired denoisers: a residual CNN with conservation constraints, its Gaussian-noise variants, and the Weighted Inductive Pileup-physics-inspired U-Network for Denoising (WIPUNet), which integrates these ideas into a UNet backbone. On CIFAR-10 with Gaussian noise at $σ\in\{15,25,50,75,100\}$, PU-inspired CNNs are competitive with standard baselines, while WIPUNet shows a \emph{widening margin} at higher noise. Complementary BSD500 experiments show the same trend, suggesting physics-inspired priors provide stability where purely data-driven models degrade. Our contributions are: (i) translating pileup-mitigation principles into modular inductive biases; (ii) integrating them into UNet; and (iii) demonstrating robustness gains at high noise without relying on heavy SOTA machinery.