Multi-domain Foveated Compressive Sensing for Adaptive Imaging
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Solution Overview
Problem
Current imaging systems are limited by sensor pixel count, SWAP constraints, and communication bandwidth, and existing compressive sensing systems do not adapt to specific tasks or utilize prior knowledge, leading to inefficiencies and limitations in spatial and temporal resolution, especially in surveillance and monitoring applications.
Innovation Solution
A multi-domain foveated compressive sensing system using a digital micro-mirror device (DMD) with dual-path optical design for adaptive imaging in intensity, polarization, and spectral domains, allowing for variable resolution and high compression ratios without light loss, achieved through a combination of DMD micro-mirror arrays, patch deflector mirrors, and wavelength foveated detectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional Nyquist sampling is used with sensor arrays, then complete image information is captured, but sensor pixel count, SWAP constraints, and communication bandwidth requirements increase
Solution Approach 1:
The patent extracts only the essential image information needed for specific tasks rather than capturing complete image data. By identifying and measuring only the critical features and regions of interest, the system reduces the quantity of data that needs to be stored and transmitted while maintaining measurement precision for task-relevant information
Solution Approach 2:
The system performs preliminary processing by applying compressive sensing measurements before full image reconstruction. The measurement matrix is designed in advance to capture sufficient information for task-specific requirements, allowing the system to obtain adequate image quality without requiring complete pixel-level data acquisition
2Measurement precision
If sensor array size is increased to improve image resolution, then spatial resolution improves, but SWAP constraints and bandwidth requirements increase
Solution Approach 1:
The patent changes the measurement parameters by using compressive sensing with randomized measurement matrices instead of traditional pixel-by-pixel sampling. This allows the system to achieve equivalent or superior spatial resolution with fewer measurements by exploiting the sparsity of natural images in certain transform domains, thereby reducing sensor array size while maintaining resolution
3Quantity of substance
If compressive sensing is used to reduce measurements, then bandwidth and SWAP requirements decrease, but image reconstruction accuracy and quality may be compromised
Solution Approach 1:
The system performs preliminary optimization by designing measurement matrices and selecting sparsity-promoting transforms based on prior knowledge of the imaging task and image characteristics. This preliminary configuration ensures that the reduced measurements captured through compressive sensing contain sufficient information for accurate reconstruction, maintaining image quality while reducing bandwidth
Solution Approach 2:
The patent incorporates feedback mechanisms where the reconstruction quality is evaluated and used to adjust the measurement process. By monitoring reconstruction accuracy and adapting the measurement matrix or sampling rate accordingly, the system ensures that bandwidth reduction does not compromise image reconstruction fidelity beyond acceptable thresholds
4Device complexity
If existing CS systems are used without task adaptation, then implementation is simpler, but efficiency in hardware and computational resource utilization decreases
Solution Approach 1:
The patent introduces dynamic adaptability by allowing the measurement matrix and processing parameters to be adjusted based on the specific imaging task and scene characteristics. This dynamic configuration optimizes hardware resource utilization for different applications while maintaining a relatively simple base implementation structure, achieving high efficiency without excessive complexity
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
The system achieves significantly higher space-variant ROI resolution, reduces data bandwidth by 10× to 100×, and requires fewer detector elements, enabling efficient adaptive imaging with low latency and scalable SWAP-C benefits for various applications, including surveillance and autonomous systems.
Implementation Method 1
each individual DMD mirror has two tilt states for dividing the input light into two paths, a spatial intensity/polarization path and a spectral path
Implementation Method 2
a grating device receives the patch rows and generates individual patch spectrums in a vertical direction
Data Source
AI summary
Described in this disclosure is a space-variant Multi-domain Foveated Compressive Sensing (MFCS) system for adaptive imaging with variable resolution in spatial, polarization, and spectral domains simultaneously and with very low latency between multiple adaptable regions of interest (ROIs) across the field of view (FOV). The MFCS system combines space-variant foveated compressive sensing (FCS) imaging covered by a previous disclosure with a unique dual-path high efficiency optical architecture for parallel multi-domain compressive sensing (CS) processing. A single programmable Digital Micromirror Device (DMD) micro-mirror array is used at the input aperture to adaptively define and vary the resolution of multiple variable-sized ROIs across the FOV, encode the light for CS reconstruction, and adaptively divide the input light among multiple optical paths using complementary measurement codes, which can then be reconstructed as desired.