Snapshot Hyperspectral Imaging De-Blurring Algorithm
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Solution Overview
Problem
Conventional hyperspectral imaging systems are complex, bulky, expensive, and require professional skills to operate, limiting their accessibility and practicality for ordinary users. Additionally, these systems have limited spectral resolution and are slow due to the need for separate measurements at each wavelength.
Innovation Solution
A snapshot hyperspectral imaging method that uses a simple, portable device capable of dispersing incident light and a sensor to capture images. The method involves selecting reference wavelengths, rectifying dispersion, estimating relative dispersion, generating dispersion and spectral response matrices, deblurring images, and reconstructing accurate spatial hyperspectral data using strong prior constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional hyperspectral imaging systems use custom hardware and complex optical paths, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent uses a camera to capture dispersed spectral images, creating a digital copy of the spectral information rather than requiring complex physical optical paths. The dispersed light patterns are captured and processed computationally to reconstruct hyperspectral data, replacing physical complexity with computational processing.
Solution Approach 2:
The patent replaces complex mechanical optical systems with a simpler system consisting of a light source, dispersing element, and camera. The computational algorithms substitute for the complex mechanical adjustments and professional engineering manipulations previously required, enabling automated hyperspectral imaging.
2Measurement precision
If conventional scanning systems measure each wavelength separately, then spectral resolution is improved, but productivity decreases
Solution Approach 1:
The patent captures all spectral information simultaneously in a single snapshot using a dispersing element that spatially separates wavelengths. The dispersed spectral images are captured all at once rather than sequentially, and computational algorithms then process this pre-captured data to extract spectral information at full resolution.
Solution Approach 2:
The patent disperses light in the spatial dimension, mapping different wavelengths to different spatial positions in the captured image. This allows simultaneous capture of multiple wavelengths across the spatial dimension, transforming the sequential measurement problem into a parallel spatial encoding that can be processed computationally.
3Measurement precision
If CASSI systems use coded aperture masks and collimated optical paths, then spectral information reconstruction is improved, but device complexity increases
Solution Approach 1:
The patent extracts and removes the coded aperture mask and collimation requirements from the system. Instead of using these complex components, the invention directly captures dispersed spectral images with a simple optical path consisting of a light source, dispersing element, and camera sensor.
Solution Approach 2:
The patent replaces expensive, complex optical components with simpler, more affordable alternatives. The system uses standard camera sensors and simple dispersing elements rather than specialized coded aperture masks and precision collimation optics, making the system more accessible and practical.
4Measurement precision
If conventional systems require professional engineering skills for real-time adjustment, then measurement precision is improved, but ease of operation worsens
Solution Approach 1:
The patent makes the system self-calibrating and automatically reconstructing spectral information through computational algorithms. The system performs its own optimization and reconstruction without requiring professional engineering skills for real-time adjustment, enabling ordinary users to operate it easily.
Solution Approach 2:
The patent uses optimization algorithms that iteratively refine the reconstructed spectral information based on feedback from the captured dispersed images. This automated feedback loop replaces manual professional adjustment, allowing the system to self-optimize and achieve high accuracy without user intervention.
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
The method enables high-accurate hyperspectral imaging with a low-cost, user-friendly system that is faster than conventional scanning systems. It simplifies the imaging process, allowing for easy reconstruction of hyperspectral data using software alone, and ensures the accuracy of the reconstructed data through the use of strong prior constraints.
Implementation Method 1
uses a simple, portable device capable of dispersing incident light
Data Source
AI summary
A snapshot hyperspectral imaging method includes the steps of: S1, selecting a set of reference wavelengths for calibration, rectifying the shifted positions due to dispersion at each reference wavelength, and selecting a center wavelength; S2, estimating relative dispersion at each reconstructed wavelength with respect to the center wavelength; S3, generating a dispersion matrix describing the direction of dispersion, and generating a spectral response matrix using a spectral response curve of a sensor; S4, capturing images blurred with dispersion; S5, deblurring the dispersed images captured in S4 using the dispersion matrix and the spectral response matrix generated in S3 to obtain spectral data spatially aligned in all spectrums; and S6, projecting the aligned spectral data obtained in S5 into color space, extracting a foreground image by a threshold method, sampling the dispersed images obtained in S4 as strong prior constraints for the foreground image, and reconstructing accurate spatial hyperspectral data.