Microscope Data Recovery via Photon Counting and Inverse PSF
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Solution Overview
Problem
Current deconvolution methods for microscope images face limitations in recovering microstructure information outside the band and struggle to improve both spatial and time resolution simultaneously, with unreliable estimation and loss of information due to filtering.
Innovation Solution
A recovery device that acquires photon detection number distribution using a high-precision measurement system, generates an inverse point spread function through out-of-band extrapolation, and calculates evaluation values to quantify reliability, allowing for improved spatial resolution without losing out-of-band information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deconvolution method with filter is used to suppress noise, then reliability of estimation is improved, but spatial resolution is degraded due to elimination of out-of-band information
Solution Approach 1:
The patent introduces an intermediary statistical model (photon detection number distribution based on Poisson distribution) that mediates between the measured image and the reconstructed image. This model allows probabilistic reconstruction that preserves out-of-band information while accounting for noise, avoiding the need for aggressive filtering that would lose high-frequency spatial information.
Solution Approach 2:
The patent changes the fundamental parameter from intensity-based reconstruction to photon count-based probabilistic reconstruction. By modeling the detection process statistically and using maximum likelihood estimation, the system can recover out-of-band frequency information with quantified reliability, resolving the trade-off between resolution and reliability.
2Productivity
If point estimation method is used for deconvolution, then calculation speed is improved, but reliability of recovered information is degraded
Solution Approach 1:
The patent implements feedback through iterative maximum likelihood estimation, where the reconstructed image is continuously refined by comparing predicted photon distributions with actual measurements. This iterative process provides feedback that improves reliability while maintaining computational efficiency through optimized convergence criteria.
Solution Approach 2:
The patent applies partial action by performing deconvolution only to the extent necessary to recover meaningful out-of-band information, using statistical significance thresholds to stop reconstruction when additional processing no longer improves reliability. This avoids excessive computation while maintaining adequate reliability.
3Manufacturing precision
If SIM or Localization method is used to improve spatial resolution, then spatial resolution is enhanced, but time resolution is degraded
Solution Approach 1:
The patent extracts only the essential statistical information (photon detection numbers) from the imaging process, discarding redundant intensity measurements. This extraction approach enables super-resolution reconstruction from standard microscope images without requiring the complex temporal sequences needed by SIM or localization methods, thus preserving time resolution.
4Object-affected harmful factors
If out-of-band information is eliminated by filter, then noise is suppressed, but recoverable microstructure information is limited
Solution Approach 1:
The patent converts the harmful effect of noise into beneficial statistical information by modeling photon detection as a Poisson process. The noise characteristics become part of the statistical model that enables reliable reconstruction, turning what was previously a limitation into a feature that guides the reconstruction process while preserving out-of-band microstructure information.
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
Enables quantitative evaluation of reliability and enhanced spatial resolution in microscope images, preventing information loss and maintaining high-resolution imaging capabilities.
Implementation Method 1
a diffraction causes blur in a spatial structure of an observation object
Implementation Method 2
the photon detection number distribution is acquired by the photon counting
Data Source
AI summary
Provided is a data recovery device, having: an acquiring unit that acquires photon detection number distribution of an image acquired from an imaging optical system; a recovering unit that acquires an estimated image from the photon detection distribution using a predetermined IPSF (an inverse function of a point spread function PSF); an evaluation value calculating unit that calculates, in relation to each of the estimated image and a plurality of images similar to the estimated image, an evaluation value indicating a likelihood that the image is an actual image; and an outputting unit that generates and outputs a physical parameter with which the evaluation value is at least a significance level.