Non-invasive Scattering Imaging Beyond Memory Effect Range
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Solution Overview
Problem
Existing scattering imaging methods are limited by the memory effect range, restricting non-invasive imaging of objects with large viewing angles, especially when using memory effect-based technologies, and require invasive calibration or large equipment for ballistic light-based methods.
Innovation Solution
A non-invasive scattering imaging method based on connected component optimization, which collects speckle information, calculates autocorrelation images, optimizes reconstruction results using phase recovery algorithms, and separates mixed signals through linear subtraction, allowing imaging beyond the memory effect range without prior calibration or extensive equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If memory effect-based imaging methods are used, then non-invasive imaging of scattering media is achieved, but imaging range is limited by memory effect range
Solution Approach 1:
The patent segments the mixed speckle signal into multiple individual object signals by calculating autocorrelation functions and using connected component analysis to separate overlapping autocorrelations. This allows recovery of objects beyond the traditional memory effect range by processing the composite speckle pattern as separable components rather than a single mixed signal.
Solution Approach 2:
The patent transforms the problem from spatial domain imaging to autocorrelation domain analysis. By calculating the autocorrelation function of the speckle pattern, the method maps spatial information to a different mathematical domain where objects with aliased autocorrelations can be separated and individually reconstructed, extending the effective imaging range.
2Measurement precision
If ballistic light-based imaging methods are used, then imaging precision is improved, but equipment complexity increases and scattering effect must be weak
Solution Approach 1:
The patent replaces complex mechanical/optical calibration systems with a computational approach based on autocorrelation analysis. Instead of using guiding stars or system calibration procedures, the method uses mathematical processing of the speckle autocorrelation function to achieve precise object localization and reconstruction, simplifying the overall system while maintaining precision.
3Measurement precision
If wavefront shaping or deconvolution methods are used, then imaging quality is improved, but invasive calibration or guiding stars are required
Solution Approach 1:
The patent implements a self-service approach where the speckle autocorrelation function itself provides the information needed for object separation and reconstruction. The method uses the inherent statistical properties of the speckle pattern and connected component analysis to automatically separate mixed signals without requiring external calibration sources, guiding stars, or invasive procedures.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Achieves accurate separation and recovery of objects with aliased autocorrelation, enabling non-invasive imaging of multiple objects with wide viewing angles, improving the imaging range by simplifying autocorrelation as a linear superposition of individual object autocorrelations and requiring only single-shot sampling.
Implementation Method 1
After light passes through a scattering medium, original information is seriously disturbed
Implementation Method 2
calculating an autocorrelation image of the speckle information collected in step A1, and obtaining an autocorrelation graph according to the autocorrelation image
Data Source
AI summary
A non-invasive scattering imaging method beyond a memory effect range based on connected component optimization, including: calculating an autocorrelation image of collected speckle information, and obtaining an autocorrelation graph according to the autocorrelation image; obtaining a reconstruction result according to the autocorrelation graph, and optimizing the reconstruction result; calculating and normalizing autocorrelation of the optimized reconstruction result, to calculate initial autocorrelation of an object; obtaining a reconstruction result according to the autocorrelation of the object obtained in the previous step, calculating autocorrelation of an optimized reconstruction result by using a connected component and performing normalization, and using the autocorrelation graph to calculate autocorrelation of another object; and repeating the preceding steps of calculating autocorrelation of the two objects until a predetermined loop count is reached, and then using a phase recovery algorithm and the calculated autocorrelation of the two objects to perform spatial reconstruction to achieve non-invasive imaging.