A Contrastive Pre-trained Foundation Model for Deciphering Imaging Noisomics across Modalities

TL;DR

This paper introduces CoP, a contrastive pre-trained model that effectively decodes imaging noise across modalities using minimal data, outperforming traditional methods and enabling advanced diagnostics without extensive training.

Contribution

The paper presents a novel contrastive learning framework that significantly reduces data requirements and improves noise decoding accuracy across diverse imaging modalities.

Findings

Achieves superior performance with only 100 training samples.

Outperforms supervised models trained on 100,000 samples.

Demonstrates robust zero-shot generalization across 12 datasets.

Abstract

Characterizing imaging noise is notoriously data-intensive and device-dependent, as modern sensors entangle physical signals with complex algorithmic artifacts. Current paradigms struggle to disentangle these factors without massive supervised datasets, often reducing noise to mere interference rather than an information resource. Here, we introduce "Noisomics", a framework shifting the focus from suppression to systematic noise decoding via the Contrastive Pre-trained (CoP) Foundation Model. By leveraging the manifold hypothesis and synthetic noise genome, CoP employs contrastive learning to disentangle semantic signals from stochastic perturbations. Crucially, CoP breaks traditional deep learning scaling laws, achieving superior performance with only 100 training samples, outperforming supervised baselines trained on 100,000 samples, thereby reducing data and computational dependency by three orders of magnitude. Extensive benchmarking across 12 diverse out-of-domain datasets confirms its robust zero-shot generalization, demonstrating a 63.8% reduction in estimation error and an 85.1% improvement in the coefficient of determination compared to the conventional training strategy. We demonstrate CoP's utility across scales: from deciphering non-linear hardware-noise interplay in consumer photography to optimizing photon-efficient protocols for deep-tissue microscopy. By decoding noise as a multi-parametric footprint, our work redefines stochastic degradation as a vital information resource, empowering precise imaging diagnostics without prior device calibration.