Pan-sharpening for Microscopy via Coupled Non-negative Matrix Factorization
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Solution Overview
Problem
Current microscopy techniques face challenges in acquiring high spatial and spectral resolution images efficiently, as they require capturing full spectra at each spatial resolution point, leading to increased time and potential sample drift or degradation, and existing data fusion methods can generate reconstruction artifacts due to differences in image formation mechanisms across channels.
Innovation Solution
A system that generates full-spatial resolution, full-spectral resolution images within a given spectral range by acquiring a set of first-spatial resolution monochromatic images and second-spatial resolution spectral maps, using a processor to combine these using a restoration procedure, such as coupled non-negative matrix factorization, to produce a 3D spectral-data cube, allowing for the generation of images at any target spectral value without needing all full-spatial resolution, full-spectral resolution data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full spectra are captured at each spatial resolution point, then high spectral and spatial resolution images are obtained, but acquisition time increases proportionally with the number of acquisition points
Solution Approach 1:
The patent acquires only a subset of spectral data points rather than complete spectra at all spatial locations. By selecting specific wavelengths to measure at high spatial resolution and using lower spatial resolution measurements at other wavelengths, the method obtains sufficient information to reconstruct full spectral cubes without the time cost of complete sampling at all points.
Solution Approach 2:
The spectral data acquisition is divided into multiple channels with different spatial resolutions. The patent segments the spectral range and assigns different spatial sampling strategies to different spectral bands, allowing efficient data collection that can be later integrated through computational methods to produce full-resolution spectral images.
2Loss of information
If full spectra are captured at each spatial resolution point, then complete spectral information is obtained, but sample drift and degradation occur
Solution Approach 1:
The patent acquires only the minimum necessary spectral data points to reconstruct complete spectral information. By measuring fewer spectral channels at high spatial resolution and supplementing with lower-resolution data, the total measurement time is reduced, thereby minimizing sample drift and degradation while still obtaining complete spectral cubes through computational reconstruction.
3Productivity
If data fusion is used to reconstruct full-resolution datasets, then acquisition time is reduced, but reconstruction artifacts are generated due to different image formation mechanisms
Solution Approach 1:
The patent transforms the data from different spatial resolution channels into a common spectral space using mathematical transformations. By changing the representation parameters and using constrained optimization with non-negativity constraints, the method integrates data from different measurement conditions without introducing artifacts from direct fusion of incompatible data formats.
Data Source
AI summary
Techniques for generating full-spatial resolution, full spectral resolution image(s) from a 3D spectral-data cube for any spectral value within a given spectral range are provided without requiring the acquisition of all full-spatial resolution, full spectral resolution data by an instrument. The 3D spectral-data cube is generated from a limited number of full-spatial resolution, sparse spectral resolution data and a sparse-spatial resolution, full-spectral resolution data of the same area of the sample. The use of the 3D spectral-data cube reduces the data acquisition time.