Foveal Compressive Upsampling for Low-Data-Rate Imaging
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Solution Overview
Problem
Wide-area persistent surveillance at high resolutions generates high sensor-to-system data rates and power dissipation rates, exceeding the capacity of cooling systems, particularly for cooled sensors, leading to complex systems or slow revisit rates.
Innovation Solution
Foveal compressive upsampling is implemented by dividing a focal plane array or sensor array into detector groups corresponding to block pixels, applying mask patterns to select and aggregate detector outputs, and using compressive sensing algorithms to reconstruct full-resolution images, offering significant data compression and reduced power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high-resolution data is captured at video rates or higher frame rates, then imaging resolution and frame rate are improved, but sensor-to-system data rates and power dissipation rates increase excessively
Solution Approach 1:
The sensor array is divided into multiple detector groups, where each group corresponds to a block pixel. Multiple detectors within each group share a common readout path, segmenting the high-resolution sensor into manageable blocks that can be processed at lower data rates while maintaining overall high-resolution imaging capability at video frame rates.
Solution Approach 2:
Multiple detectors are combined into detector groups that share common readout circuitry and cooling infrastructure. By merging multiple detector outputs into fewer readout channels, the system reduces the total data transmission rate and power consumption while preserving the ability to reconstruct high-resolution images through computational methods.
2Productivity
If high-resolution data is captured at video rates or higher frame rates, then imaging resolution and frame rate are improved, but sensor-to-system data rates increase excessively
Solution Approach 1:
The high-resolution sensor array is segmented into detector groups with shared readout paths. This segmentation allows the system to capture high-resolution data at video rates by processing blocks of pixels through common readout channels, thereby reducing the total data transmission rate while maintaining the ability to reconstruct full-resolution images.
Solution Approach 2:
The system changes the readout parameter by using shared readout paths for multiple detectors, effectively reducing the data rate parameter. By altering how data is read out and processed through detector groups rather than individual detector readout, the system maintains high-resolution capture capability while reducing the actual data transmission rate.
3Use of energy by moving object
If multiple detectors are assigned to a detector group with shared readout paths, then power consumption and data rates are reduced, but individual detector resolution may be compromised
Solution Approach 1:
The sensor array is segmented into detector groups where multiple detectors share common readout paths. This segmentation reduces power consumption and data rates by eliminating redundant readout circuitry for each individual detector, while the segmented structure preserves measurement precision through computational reconstruction methods that recover individual detector information from the grouped measurements.
Solution Approach 2:
The system uses feedback through computational reconstruction algorithms that process the aggregated detector group data to recover high-resolution information. The feedback mechanism allows the system to maintain measurement precision equivalent to individual detectors by using the patterned measurements from detector groups to reconstruct what each individual detector would have measured.
Data Source
AI summary
An apparatus includes a sensor having an array of detectors. The sensor is configured to assign multiple detectors to a detector group corresponding to a block pixel. The sensor is also configured, for each frame of a set of frames, to apply a specified one of a set of mask patterns in order to select outputs of the detectors in the detector group and aggregate the selected outputs of the detectors in the detector group to determine pixel information for the block pixel. The apparatus also includes at least one processor configured to generate the frames using the pixel information for the block pixel, and upscale the portion of the at least one of the frames using the set of mask patterns to identify native pixels within the block pixel.