Spectral Filter Array for Snapshot Hyperspectral Imaging
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Solution Overview
Problem
Current hyperspectral imaging technologies face challenges in achieving high resolution and efficient data acquisition due to the need for scanning and the high number of measurements required, which results in costly and complex systems, especially for snapshot spectral cameras.
Innovation Solution
A method utilizing a uniformly and aperiodically distributed Spectral Filter Array (SFA) with interferometric filters, combined with computational reconstruction methods like deconvolution and non-linear sparse reconstruction, to acquire and reconstruct 3D spectral data cubes from a 2D dataset, reducing the number of required measurements and system complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scanning methods (pushbroom, band sequential, interferometry) are used to acquire spectral data, then spectral resolution is improved, but acquisition time and system complexity increase
Solution Approach 1:
The spectral filter array divides the spectral range into multiple bands by placing multiple spectral filters at different locations across the sensor array. Each filter element captures a specific spectral band simultaneously across the entire spatial field, eliminating the need for temporal scanning while maintaining spectral resolution.
Solution Approach 2:
The patent transitions from temporal scanning (1D time dimension) to spatial filtering (2D spatial distribution of filters). By distributing spectral filters across the imaging sensor plane, the system achieves simultaneous spectral and spatial sampling, converting the acquisition problem from sequential to parallel.
2Loss of time
If snapshot spectral cameras use 2D diffraction grids or small filter banks, then acquisition time is reduced, but spectral resolution deteriorates
Solution Approach 1:
Instead of using a single 2D diffraction grid or small filter bank, the patent employs a comprehensive spectral filter array with multiple spectral filters distributed across the entire sensor plane. This segmentation of the spectral sampling function across many filter elements enables both snapshot acquisition and high spectral resolution simultaneously.
Solution Approach 2:
The patent changes the spectral sampling parameters by using multiple filters with different central wavelengths and bandwidths distributed across the sensor. This parameter distribution allows the system to capture multiple spectral bands simultaneously at each spatial pixel, achieving high spectral resolution without sacrificing snapshot capability.
3Loss of information
If Nyquist sampling is applied to acquire full hyperspectral data cubes, then data completeness is improved, but number of measurements and system complexity increase
Solution Approach 1:
The patent applies partial sampling by using a spectral filter array with a limited number of filters at each spatial location. Instead of requiring full Nyquist sampling across all spectral bands, the system uses a manageable number of spectral filters that provide sufficient spectral information for most applications, reducing the measurement burden while maintaining data completeness.
Solution Approach 2:
The spectral filter array implements local quality by allowing different spectral sampling densities at different spatial locations. Each spatial pixel can have a customized set of spectral filters based on local requirements, enabling adaptive sampling that reduces overall measurement complexity while maintaining data completeness where needed.
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 enables the creation of compact, lightweight, and cost-effective spectral imaging systems that achieve higher resolution spectral data cubes with significantly reduced data acquisition, facilitating applications in agriculture and environmental monitoring while lowering production and operational costs.
Implementation Method 1
configuring each SFA element of the plurality of SFA elements to filter one or more spectral bandwidths centered each at specific wavelengths
Implementation Method 2
An optical element, such as a lens: to focus the optical scene onto an imaging plane
Implementation Method 3
A dispersive element, such as a prism or diffraction grid: to spatially distribute spectral information from the imaging plane
Implementation Method 4
An imaging sensor, such as a CCD or a CMOS: to spatially sample the dispersed light
Data Source
AI summary
A method for obtaining spectral imaging data comprises at least the steps of receiving a sample set of data generated by sampling a spectral property of an image of an object in a spatial basis, wherein the sampling of the spectral property of the image of the object comprises providing a Spectral Filter Array (SFA) by arranging a plurality of SFA elements together to form a surface; configuring each SFA element of the plurality of SFA elements to filter one or more spectral bandwidths centered each at specific wavelengths corresponding to that SFA element, whereby all of the plurality of SFA elements taken together cover a determined spectral range; and setting the specific wavelengths of each SFA element of the plurality of SFA elements on the surface such to obtain a uniform and aperiodic spatial distribution of all of the plurality of SFA elements across the surface. The sampling of the spectral property of the image of the object further comprises providing an image sensor configured to record at each pixel the light filtered by one of the plurality of SFA elements or a subset of the plurality of SFA elements thereby producing one intensity value of light filtered by the one of the plurality of elements or the subset of the plurality of SFA elements per pixel; forming the image of the object on the SFA through a lens or group of lenses; and recording for all of the pixels of the image sensor the spectrally filtered intensity values thereby obtaining a 2-dimensional array of the intensity values corresponding to the image of the object. The method for obtaining spectral imaging data further comprises the step of reconstructing a full 3 dimensional spectral data cube of the imaged object from the sampled 2-dimensional array.