Spectral Density Reconstruction via Dimensionality Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computed tomography imaging spectroscopy (CTIS) systems face challenges in achieving real-time reconstruction of high-resolution images due to high processing power and memory requirements, limiting their scalability in both spectral and spatial domains.
Innovation Solution
An imaging apparatus and method that utilize circuitry to obtain object image data subject to a diffraction grating process, and input it into a spectral density reconstruction algorithm to numerically solve equations describing the optical path transformation, reducing dimensionality based on symmetry to reconstruct the spectral density of objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral density reconstruction is performed using known systems, then spectral density can be reconstructed, but processing power and memory requirements increase significantly, limiting scalability to higher resolutions
Solution Approach 1:
The patent applies dimensionality reduction by transforming the reconstruction problem from a high-dimensional spectral-spatial domain to a lower-dimensional representation. The spectral density function is represented using a reduced set of basis functions or parameters, significantly decreasing the computational complexity while maintaining reconstruction quality. This allows high-resolution spectral density reconstruction without proportionally increasing processing power and memory requirements.
2Measurement precision
If known systems reconstruct spectral density, then reconstruction can be achieved, but real-time reconstruction becomes difficult or impossible due to high processing requirements
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing lookup tables or pre-derived transformation matrices that facilitate rapid spectral density reconstruction. The complex optical path transformation is pre-characterized, allowing the actual reconstruction process to proceed much faster through simplified operations during real-time imaging, thus achieving real-time reconstruction without sacrificing accuracy.
Solution Approach 2:
The patent changes the parameters of the reconstruction algorithm by using optimized basis functions, compressed representation formats, or modified transformation equations that reduce computational complexity. This parameter optimization enables the same reconstruction accuracy to be achieved with significantly reduced processing time, making real-time reconstruction feasible.
3Manufacturing precision
If spectral density reconstruction is performed at high resolution, then image quality improves, but memory resources required increase, preventing scalability
Solution Approach 1:
The patent extracts only the essential information needed for spectral density reconstruction by using selective sampling or compression techniques. Instead of storing and processing the complete high-dimensional spectral-spatial data cube, the system extracts and retains only the critical spectral components and spatial information, significantly reducing memory requirements while preserving high-resolution reconstruction capability.
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
Enables fast and high-resolution reconstruction of spectrally and spatially multiplexed signals without increasing memory resources, overcoming the limitations of existing systems in processing power and memory consumption.
Implementation Method 1
light stemming from an image of an object, which can be placed in a field stop, passes through a diffraction grating. This results in a diffraction pattern and thereby the spectral density of the object is multiplexed into a 2d observation plane
Data Source
AI summary
The present disclosure generally pertains to an imaging apparatus for computed tomography imaging spectroscopy, which has circuitry configured to:obtain object image data being representative of light stemming from an object and being subject to an optical path and to a multiplexing process caused by a diffraction grating; andperform a spectral density reconstruction from an image of the object by inputting the obtained object image data into an spectral density reconstruction algorithm being configured to numerically solve a first equation describing the transformation of the light stemming from the object caused by the optical path into the object image based on a reduction of a dimensionality of a first function indicative of the optical path based on a symmetry of the first function, thereby reconstructing the spectral density of the object.