Benchmark study of deep super-resolution models for digital holography: quantitative phase and intensity evaluation
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Despite good axial resolution in holographic microscopy, lateral resolution remains constrained by optical limitations, pixel size, and noise. These restrictions hinder accurate reconstruction of fine structural and phase details. To address this, we evaluate three deep-learning super-resolution models (RCAN, SwinIR, and a conditional diffusion network) on 1,440 off-axis digital holograms of microbeads downsampled by 2 ×, 3 ×, and 4 ×. We compare their performance to bicubic spline interpolation using PSNR, SSIM, MSE, and phase-derived depth errors. RCAN and SwinIR yield the most accurate reconstructions, preserving structural and quantitative phase information, and offering guidance on model selection for phase-focused holography.
Date Issued
2025-09-08
Date Acceptance
2025-08-20
Citation
Optics Express, 2025, 33 (18), pp.38696-38706
ISSN
1094-4087
Publisher
Optica Publishing Group
Start Page
38696
End Page
38706
Journal / Book Title
Optics Express
Volume
33
Issue
18
Copyright Statement
© 2025 Published by Optica Publishing Group under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/40984272
PII: 576264
Subjects
FIELD
Optics
Physical Sciences
Science & Technology
Publication Status
Published
Coverage Spatial
United States
Date Publish Online
2025-09-03