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Updated: Mar 12, 2026

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

Multi-scale Adaptive Computational Ghost Imaging.

Shuai Sun^1,2, Wei-Tao Liu^1,2, Hui-Zu Lin^1,2

¹College of Science, National University of Defense Technology, Changsha, 410073, P. R. China.

Scientific Reports

|November 15, 2016

Summary

This summary is machine-generated.

This study introduces a multi-scale adaptive imaging method using computational ghost imaging. It efficiently captures wide scenes and zooms in for high-resolution details, significantly reducing data processing resource consumption.

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Last Updated: Mar 12, 2026

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Area of Science:

Optics and Photonics
Computational Imaging
Image Processing

Background:

Traditional imaging often requires a trade-off between wide spatial range and high spatial resolution.
Achieving both simultaneously demands high-performance detectors and substantial data processing resources.
Existing computational ghost imaging methods can be resource-intensive.

Purpose of the Study:

To develop a novel multi-scale adaptive imaging method.
To enable efficient acquisition of both broad scene outlines and high-resolution details.
To reduce the computational and resource demands compared to conventional imaging techniques.

Main Methods:

Proposal and demonstration of a multi-scale adaptive imaging approach.
Leveraging the principles of computational ghost imaging.
Utilizing a spatial light modulator to control the field of view and coherence width of illumination.
Adaptive selection of regions for high-resolution imaging.

Main Results:

Successful demonstration of the multi-scale adaptive imaging method.
Acquisition of a rough outline of the entire scene.
Identification and high-resolution imaging of specific areas of interest.
Significant reduction in resource consumption compared to typical ghost imaging.

Conclusions:

The proposed multi-scale adaptive imaging method effectively balances wide-area coverage and high-resolution detail.
This technique offers a more resource-efficient solution for complex imaging scenarios.
It provides a flexible and powerful tool for various imaging applications requiring adaptive resolution.