Adjustable Point Spread Function for Hyperspectral Imaging
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Solution Overview
Problem
Current imaging spectroscopy methods for satellite imaging, such as those using Michelson interferometers or prisms, face challenges in maintaining high spatial frequency content and signal-to-noise ratio (SNR) while reducing payload mass and avoiding noise introduction in hyperspectral image generation.
Innovation Solution
A computational method that utilizes an optical system with an adjustable point spread function to generate hyperspectral images from multiple panchromatic images, employing Fourier transforms and matrix equations to determine wavenumber content without the need for extra hardware like interferometers or filters, using a sparse aperture optical system with independently adjustable subapertures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a Michelson interferometer is used for imaging spectroscopy, then the wavenumber content can be determined, but the payload mass increases and some light is lost in the splitting operation
Solution Approach 1:
The patent extracts the interferometer function from the optical path by using computational methods. Instead of physically implementing a Michelson interferometer to separate wavenumbers, the system uses multiple panchromatic images with different point spread functions and processes them computationally through Fourier transforms and matrix equations to determine wavenumber content, thereby eliminating the heavy interferometer hardware.
Solution Approach 2:
The patent replaces the mechanical interferometer system with a computational approach. The physical light splitting and interference process is substituted by digital signal processing techniques, including Fourier transforms and linear algebra operations, which can be performed after the images are captured by the imaging system.
2Measurement precision
If a Michelson interferometer is used for imaging spectroscopy, then the wavenumber content can be determined, but some light is lost in the splitting operation
Solution Approach 1:
The patent removes the light-splitting interferometer component entirely, allowing all incident light to reach the imaging sensor. The wavenumber separation function is extracted from the optical domain and implemented computationally, eliminating the energy loss associated with beam splitting and interference optics.
3Measurement precision
If component panchromatic images are obtained using a Michelson interferometer, then the wavenumber content can be determined, but the signal-to-noise ratio becomes very low
Solution Approach 1:
The patent performs preliminary actions by capturing multiple panchromatic images with different point spread functions before computational processing. These images contain the full spectral information needed for wavenumber determination, and by acquiring them with high SNR using standard imaging optics (rather than interferometric methods), the system preserves signal quality while enabling spectral analysis through subsequent computational steps.
4Device complexity
If the optical array is moved relative to another portion to obtain interferometer effects, then the interferometer is eliminated, but the high spatial frequency content is limited
Solution Approach 1:
The patent uses a dynamic, adjustable point spread function that can be modified independently for each image. By controlling the optical system to produce different PSFs (e.g., through varying focus or optical path differences), the system captures diverse spectral information without mechanical interferometer components, while maintaining full spatial frequency content through the imaging system's optical transfer function.
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
This approach allows for high-resolution multispectral or hyperspectral image generation without additional hardware, maintaining high SNR and preserving spatial frequencies, thus enhancing image quality and reducing noise.
Implementation Method 1
an optical system having an adjustable, wavenumber-dependent point spread function
Implementation Method 2
each panchromatic image corresponding to a selected one of a predetermined set of point spread functions and being comprised of a measured intensity data set
Implementation Method 3
transforming the collected plurality of panchromatic images from an image domain into the spatial frequency domain by using a Fourier transform
Implementation Method 4
solving a matrix equation at each one of a predetermined set of spatial frequencies, in which a vector of the transformed panchromatic images at each spatial frequency is equal to the product of a predetermined matrix of discrete weighting coefficients and a vector representing a wavenumber content
Implementation Method 5
inverse transforming the determined wavenumber content of the image source from the spatial frequency domain into the image domain, resulting in the hyperspectral image of the image source
Data Source
AI summary
Generating a multispectral or hyperspectral image of an image source with an optical system having an adjustable, wavenumber-dependent point spread function, by collecting panchromatic images of the image source, each of which corresponds to a selected point spread function and includes a measured intensity data set corresponding to a range of wavelengths, transforming the panchromatic images into the spatial frequency domain by using a Fourier transform, solving a matrix equation at each spatial frequency, in which a vector of the transformed panchromatic images is equal to the product of a predetermined matrix of discrete weighting coefficients and a vector representing a wavenumber content of the image source at each spatial frequency, resulting in a determined wavenumber content of the image source in the spatial frequency domain, and inverse transforming the determined wavenumber content of the image source from the spatial frequency domain into the image domain, resulting in the multispectral or hyperspectral image.