Hyperspectral Data Reconstruction from RGB Images
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Solution Overview
Problem
Hyperspectral imaging systems face limitations due to high costs, excessive size, and reduced spatial, spectral, and temporal resolution, making them unsuitable for general computer vision and natural image analysis, particularly in applications requiring high spatial or temporal resolution.
Innovation Solution
A method and apparatus for approximating spectral data using a dictionary of hyperspectral signatures and values in RGB images, allowing for the reconstruction of hyperspectral data from RGB images without the need for hyperspectral equipment, and an optimized color filter array selection for improved photon efficiency and spectral reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hyperspectral imaging systems are used to acquire complete spectral signatures, then spectral information completeness is improved, but spatial resolution and temporal resolution deteriorate
Solution Approach 1:
The patent creates a computational copy of hyperspectral data by training a neural network on limited hyperspectral images to generate synthetic spectral signatures. This allows RGB images to be transformed into hyperspectral representations without physical hyperspectral sensors, thus maintaining spatial resolution while achieving spectral information completeness through software-based spectral reconstruction
Solution Approach 2:
The patent transforms the problem from physical spectral measurement to computational spectral generation by changing the parameter space. Instead of measuring all spectral bands directly (which reduces spatial resolution), the system uses RGB values as input parameters and generates full spectral signatures through neural network computation, thereby preserving spatial resolution while achieving spectral completeness
2Measurement precision
If hyperspectral imaging systems are used to acquire complete spectral signatures, then spectral information completeness is improved, but device cost and physical size deteriorate
Solution Approach 1:
The patent replaces expensive physical hyperspectral sensors with a computational model that copies hyperspectral data characteristics. By training a neural network on a small dataset of hyperspectral images, the system creates a software-based hyperspectral camera that runs on standard RGB cameras, eliminating the need for costly and bulky specialized hardware
Solution Approach 2:
The patent substitutes the mechanical/optical hyperspectral imaging system with a computational algorithm. Instead of using physical prisms, gratings, or filter arrays to separate spectral bands, the system uses a neural network to computationally generate spectral signatures from RGB inputs, thereby replacing complex mechanical systems with software processing
3Measurement precision
If hyperspectral imaging systems are used to acquire complete spectral signatures, then spectral information completeness is improved, but acquisition speed deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-training the neural network offline on a dataset of hyperspectral images. This training phase is done once in advance, and the resulting model can then rapidly generate spectral signatures from RGB images in real-time applications, thus achieving spectral completeness without sacrificing acquisition speed during actual use
Data Source
AI summary
A method for approximating spectral data, the method comprising using at least one hardware processor for: providing a digital image comprising data in a first set of spectral bands; providing a dictionary comprising (a) signatures in a second set of spectral bands and (b) values in said first set of spectral bands, wherein said values correspond to the signatures, and wherein said first and second sets of spectral bands are different; and approximating, based on the dictionary, data in said second set of spectral bands of said digital image.