Denoising ICF Images with Multiplicative Uniform Noise: A Self-Supervised Study Based on the Log-Domain Noisier2Inverse Framework

2026-06-25

AI Summary

This work addresses the challenging problem of denoising inertial confinement fusion (ICF) images corrupted by multiplicative homogeneous noise in the absence of clean training labels. The paper proposes Log-Domain Noisier2Inverse, the first self-supervised framework tailored to this noise model. By applying a logarithmic transformation, multiplicative noise is converted into additive noise, and the authors theoretically demonstrate that the self-supervised loss in this transformed domain is equivalent to that of supervised learning. Two variants are explored: one with fixed Gaussian approximation (Variant A) and another with image-adaptive noise parameter estimation (Variant B). On real ICF data, Variant B achieves a PSNR of 21.41 dB and SSIM of 0.8358, representing a 19.46 dB improvement over the noisy input and significantly outperforming established methods such as BM3D and Noise2Self.

Abstract

This paper documents the implementation and evaluation of a self-supervised denoising framework on Inertial Confinement Fusion (ICF) images corrupted by Multiplicative Uniform noise: the \emph{Log-Domain Noisier2Inverse} framework. This framework is developed and analysed in this work; the key theoretical result -- that minimising the log-domain self-supervised loss is equivalent to supervised learning in the transformed domain -- is presented with full proof. We document significant implementation challenges arising from the unique characteristics of ICF imagery, describe the fixes applied at each stage, and report final quantitative results. The log-domain approach with per-image JSON Uniform noise loading (Variant~B) achieves the best result: a mean PSNR of $21.41\db$ and SSIM of $0.8358$, a $+19.46\db$ improvement over the noisy input baseline of $1.95\db$, substantially outperforming BM3D log-domain ($4.47\db$, SSIM $0.5181$) and Noise2Self ($4.75\db$, SSIM $0.0177$). Variant~A, using fixed Gaussian noise loading, achieves $21.39\db$ PSNR and SSIM $0.8436$. Of the three evaluated methods, Log-Domain Noisier2Inverse and Noise2Self are entirely self-supervised during training, requiring no clean ground truth data; BM3D is a classical filter-based method requiring no training at all. The clean reference images are used solely for quantitative evaluation of all three methods.

Problem

• Denoising
• Inertial Confinement Fusion
• Multiplicative Uniform Noise
• Self-Supervised Learning
• Image Restoration

Innovation

• Log-Domain Noisier2Inverse
• self-supervised denoising
• multiplicative uniform noise
• Inertial Confinement Fusion (ICF)
• noise loading