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Super-Resolution for Interferometric Imaging: Model Comparisons and Performance Analysis

arXiv 2502.15397 ↗First posted 21 Feb 2025physics.optics cs.AIcited by 0

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Abstract (arXiv)

This study investigates the application of Super-Resolution techniques in holographic microscopy to enhance quantitative phase imaging. An off-axis Mach-Zehnder interferometric setup was employed to capture interferograms. The study evaluates two Super-Resolution models, RCAN and Real-ESRGAN, for their effectiveness in reconstructing high-resolution interferograms from a microparticle-based dataset. The models were assessed using two primary approaches: image-based analysis for structural detail enhancement and morphological evaluation for maintaining sample integrity and phase map accuracy. The results demonstrate that RCAN achieves superior numerical precision, making it ideal for applications requiring highly accurate phase map reconstruction, while Real-ESRGAN enhances visual quality and structural coherence, making it suitable for visualization-focused applications. This study highlights the potential of Super-Resolution models in overcoming diffraction-imposed resolution limitations in holographic microscopy, opening the way for improved imaging techniques in biomedical diagnostics, materials science, and other high-precision fields.

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References · 10

  1. 2022Generative Adversarial Networkscited by 5,627
  2. 2017Accurate, Large Minibatch SGD: Training ImageNet in 1 Hourcited by 1,492
  3. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,264
  4. 2021Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Datacited by 970
  5. 2017Large Batch Training of Convolutional Networkscited by 535
  6. 2022Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Modelscited by 174
  7. 2018To learn image super-resolution, use a GAN to learn how to do image degradation firstcited by 94
  8. 2022Revisiting RCAN: Improved Training for Image Super-Resolutioncited by 32
  9. 2023Quantitative phase imaging (QPI) through random diffusers using a diffractive optical networkcited by 10
  10. 2018Deep learning-based super-resolution in coherent imaging systemscited by 7

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