Spectral Filter Sweep for Event-Based Sensor Data Compression
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Solution Overview
Problem
Current spectral imaging systems face challenges in achieving high spatial, temporal, and spectral accuracy due to the high data volume generated, which leads to reduced spatial image accuracy and temporal sampling limitations, especially when using mosaicking techniques with color filters.
Innovation Solution
A spectral filter with variable transmission, an event-based imaging sensor that generates measurement events based on filter responses, and a processor that controls the filter transmission to sweep over wavelengths, allowing for the estimation of the observed spectrum through measurement events and filter transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If mosaicking with color filters is applied to reduce data volume, then data compression is achieved, but spatial image accuracy deteriorates
Solution Approach 1:
The patent applies a dynamic spectral filter that continuously sweeps through different wavelengths over time, rather than using static color filters. This temporal modulation of filter transmission allows each pixel to capture spectral information across multiple wavelengths sequentially, achieving data compression while maintaining spatial resolution through the time-varying measurement approach
Solution Approach 2:
The patent transforms the spectral imaging problem from a spatial sampling challenge to a temporal measurement problem. By adding the time dimension through continuous spectral filtering, the system captures spectral information dynamically, converting spatial accuracy requirements into temporal sampling requirements that can be satisfied without sacrificing spatial resolution
2Measurement precision
If high precision spectral measurements are captured, then spectral accuracy is improved, but data bandwidth increases
Solution Approach 1:
The patent extracts only the necessary spectral information by using a sweeping filter that sequentially probes different wavelengths. Instead of capturing the complete spectrum simultaneously for every pixel, the system extracts spectral measurements over time, reducing the instantaneous data bandwidth requirement while maintaining spectral accuracy through the temporal sequence of measurements
Solution Approach 2:
The patent changes the filter transmission parameter dynamically over time, sweeping through different wavelength ranges. This temporal variation in filter characteristics allows the system to capture spectral information with high precision while reducing data bandwidth, as each pixel outputs a time-varying signal that encodes spectral information rather than simultaneous multi-wavelength data
3Measurement precision
If globally changing color filters are used, then spectral information is improved, but temporal sampling limitation worsens
Solution Approach 1:
The patent implements periodic spectral filtering where the filter transmission sweeps through wavelengths in a continuous, repeating cycle. This periodic modulation of the filter allows the system to capture spectral information at multiple time points within each cycle, improving spectral measurement precision while mitigating temporal sampling limitations through the repeated measurement opportunities provided by each sweep cycle
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 efficient data compression and reconstruction of spectral scenes with high spatial and spectral resolution, overcoming the limitations of traditional systems by asynchronously recording intensity changes and utilizing numerical reconstruction methods like Tikhonov regularization or compressive sensing.
Implementation Method 1
a spectral filter (26) with a variable spectral filter transmission (FT(λ))
Implementation Method 2
an event-based imaging sensor (27) configured to produce measurement events (y1, y2, . . . , yn) which correspond to a change in a filter response (Y(t)) that is generated by an observed spectrum (X(λ))
Data Source
AI summary
An apparatus comprising a spectral filter (26) with a variable spectral filter transmission (FT(λ)); an event-based imaging sensor (27) configured to produce measurement events (y1, y2, . . . , yn) which correspond to a change in a filter response (Y(t)) that is generated by an observed spectrum (X(λ)), and a processor (28) configured to control the filter transmission (FT(λ)) of the spectral filter (26) so that it sweeps over wavelength (λ) with time (t), and to generate an estimation (X*(λ)) of the observed spectrum (X(λ)) based on the measurement events (y1, y2, . . . , yn) that correspond to the filter response (Y(t)) and based on the filter transmission (FT(λ)).