ArticleNature communications2026
Self-supervised image denoising and restoration method for atomic force microscopy.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- Self-supervised image denoising and restoration method for atomic force microscopy.Nature communications · 2026Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Scanning probe microscopy (SPM) distinguishes itself from light and electron microscopy by sensing surface interactions with a nanoscale probe, rather than relying on the detection of particles or waves; and has evolved into a versatile tool across several fundamental and applied research fields. SPM uses raster-scanning for image formation, comprising trace (left-to-right) and retrace (right-to-left) scans - this spatial redundancy is however usually not fully taken advantage of. Here, we introduce a self-supervised deep learning framework for Scanning Probe microscopy Image DEnoising and Restoration (SPIDER), by utilizing trace and retrace information. SPIDER improves the signal-to-noise ratio up to 3-fold and accelerates imaging speed up to 6-fold, circumventing the need for a large training set and a ground truth. We further demonstrate that SPIDER enables imaging acceleration by utilizing spatial information from the fast-scan axis to reconstruct missing spatial information in the slow-scan axis in a self-supervised manner. The self-supervised reconstruction is competitive with supervised learning methods. We anticipate that SPIDER will improve SPM imaging and inspire further applications.
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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.