Subsampled digital holographic image reconstruction by a compressive sensing approach.

Area of Science:

• Optics and Photonics
• Digital Imaging
• Signal Processing

Background:

• Digital holography enables 3D reconstruction but often requires dense sampling.
• Subsampling holograms can reduce data acquisition and storage but leads to reconstruction artifacts.
• Compressive sensing offers a framework for reconstructing signals from undersampled measurements.

Purpose of the Study:

• To develop a compressive sensing approach for digital holographic image reconstruction from subsampled holograms.
• To achieve resolution improvement and lensless magnification with enhanced noise immunity.
• To validate the method's effectiveness with unusual subsampling schemes.

Main Methods:

• The method models the sensed field as subsampled and low-pass filtered.
• Image reconstruction is performed using an inverse problem approach on a Fresnel-Bluestein base.
• Compressive sensing principles are applied to handle undersampled holographic data.

Main Results:

• Demonstrated successful in-line digital holographic image reconstruction from subsampled holograms.
• Achieved significant resolution improvement and controlled lensless magnification.
• Showcased high noise immunity in reconstructed images, validated by simulations and experiments.

Conclusions:

• The compressive sensing approach effectively reconstructs high-quality images from subsampled holograms.
• The method provides a robust solution for lensless magnification and resolution enhancement in digital holography.
• The technique is applicable even with non-standard subsampling strategies in holography.