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arXiv AI By Asya Y. Akkus, Bradley T. Wolfe, Pinghan Chu, Chengkun Huang, Chris S. Campbell, Mariana Alvarado Alvarez, Petr Volegov, David Fittinghoff, Robert Reinovsky, Zhehui Wang

A Machine Learning-Driven Solution for Denoising Inertial Confinement Fusion Images

Read the original on arXiv AI →

The paper presents an unsupervised autoencoder that uses a Cohen-Daubechies-Feauveau (CDF 97) wavelet transform in its latent space to denoise neutron imaging data from inertial confinement fusion experiments. The method targets mixed Gaussian‑Poisson noise, preserving fine details and edges that are crucial for image reconstruction. Benchmarks on simulated and experimental NIF datasets show lower reconstruction error and better edge preservation than conventional filtering techniques such as BM3D.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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