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

2025-02-21

AI Summary

To address the resolution limitation imposed by the diffraction limit in holographic microscopy, which degrades quantitative phase imaging (QPI) fidelity, this study systematically evaluates the super-resolution performance of RCAN and Real-ESRGAN on off-axis Mach–Zehnder interferometric data—the first such comparative assessment. Using experimentally acquired microparticle interferograms, we quantitatively benchmark both models via structural similarity (SSIM) and morphological phase fidelity analysis. Results show that RCAN achieves superior numerical accuracy in phase reconstruction (PSNR increase of 2.1 dB), making it preferable for quantitative analysis; in contrast, Real-ESRGAN excels in preserving structural coherence and enhancing visual interpretability (SSIM increase of 0.08), facilitating intuitive diagnostic assessment. The complementary strengths of these models establish a transferable methodological framework and provide principled guidance for model selection in high-fidelity, multi-objective optical QPI applications.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Diffusion Models for Vision
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Enhance quantitative phase imaging in holographic microscopy.
• Compare RCAN and Real-ESRGAN for interferogram reconstruction.
• Overcome diffraction limits in high-precision imaging applications.

Innovation

• Super-Resolution techniques enhance phase imaging
• Off-axis Mach-Zehnder setup captures interferograms
• RCAN and Real-ESRGAN models improve resolution