Resolution Estimation of a Digital Holographic Microscope Using Neural Network Analysis of Reconstructed Images

TL;DR

This paper introduces a neural network-based method to estimate the resolution of digital holographic microscopes from reconstructed images, effectively capturing physical degradation factors without explicit modeling.

Contribution

The novel approach uses neural networks to predict spectral bandwidth from images, enabling resolution estimation in holography without detailed physical modeling.

Findings

Model predicts spectral bandwidth with high accuracy.

Predictions align with standard resolution metrics.

Model is sensitive to different types of image degradation.

Abstract

This paper presents a method for estimating the resolution of a digital holographic microscope using neural network analysis of reconstructed images. The spectral bandwidth of the source ($\Delta \lambda$) is used as a controlled image degradation parameter. Numerical simulations were performed within inline Gabor holography. A dataset of reconstructed images was generated for several test objects over a $\Delta \lambda$ range from 0.05 to 20 nm. The model predicts $\Delta \lambda$ from reconstructed images with high precision. The predictions are consistent with standard resolution metrics, including FWHM, MTF, and the USAF resolution criterion. The generalization analysis shows that the model is sensitive to the type of degradation. It captures interferometric distortions and responds selectively to the underlying physical mechanism. The proposed approach enables resolution estimation without explicit modeling of all degradation factors and can be applied to compact holographic systems.