ArticleNewton ((New York, N.Y.)2025
Self-Supervised Elimination of Non-Independent Noise in Hyperspectral Imaging.
Article in Newton ((New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Physics-paired stimulated Raman scattering microscopy enables label-free phenotyping of lipid droplets 3D motility in live cells.Light, science & applications · 2026Article
- AI-Assisted Coherent Raman Scattering Microscopy for Clinical Translation.Chemical & biomedical imaging · 2026Review
- FILM: mapping organellar metabolism by mid-infrared photothermal-modulated fluorescence.Nature methods · 2026Article
- Resolving protein condensates and aggregates in vivo by boxcar-enhanced Fluorescence-detected mid-Infrared photothermaL Microscopy (FILM).Communications chemistry · 2026Article
- Advances in Hyperspectral Imaging for Nondestructive Food Quality and Safety Detection.Foods (Basel, Switzerland) · 2026Review
- FILM: Mapping organellar metabolism by mid-infrared photothermal modulated fluorescence.ArXiv · 2026Article
- Article
- SPEND-hSRS imaging of fumarate uncovers mitochondrial metabolic heterogeneity.bioRxiv : the preprint server for biology · 2026Article
- High-Content SRS Imaging Unveils Altered Cholesterol Metabolism in Ovarian Cancers Under CAR-T Treatment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Feeling the vibes: Vibrational spectroscopic imaging of biomolecular assemblies in their natural environment.Applied physics reviews · 2025Article
- Self-Supervised Learning and Multi-Sensor Fusion for Alpine Wetland Vegetation Mapping: Bayinbuluke, China.Plants (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Hyperspectral imaging has been widely used for spectral and spatial identification of target molecules, yet often contaminated by sophisticated noise. Current denoising methods generally rely on independent and identically distributed noise statistics, showing corrupted performance for non-independent noise removal. Here, we demonstrate Self-supervised PErmutation Noise2noise Denoising (SPEND), a deep learning denoising architecture tailor-made for removing non-independent noise from a single hyperspectral image stack. We utilize hyperspectral stimulated Raman scattering and mid-infrared photothermal microscopy as the testbeds, where the noise is spatially correlated and spectrally varied. Based on single hyperspectral images, SPEND permutates odd and even spectral frames to generate two stacks with identical noise properties, and uses the pairs for efficient self-supervised noise-to-noise training. SPEND achieved an 8-fold signal-to-noise improvement without having access to the ground truth data. SPEND enabled accurate mapping of low concentration biomolecules in both fingerprint and silent regions, demonstrating its robustness in sophisticated cellular environments.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.