ROIC Compressive Sensing for Low-Bandwidth Focal Plane Arrays
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Solution Overview
Problem
Traditional approaches to reducing bandwidth requirements for focal plane arrays (FPAs) result in increased power consumption and complexity, while existing image compression methods require sampling of every photodetector, leading to inefficiencies in capturing and transmitting images.
Innovation Solution
The integration of Compressive Sensing (CS) within Read-Out Integrated Circuits (ROICs) for FPAs, which generates compressed image data by using Compressive Sampling Measurement Matrices to selectively sample photodetectors, reducing the number of required samples and enabling efficient bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional image compression methods are used after collecting data from all photodetectors, then bandwidth requirements are reduced, but power consumption and device complexity increase
Solution Approach 1:
The patent applies preliminary action by performing compressive sensing directly at the read-out integrated circuit (ROIC) stage, before data is fully collected and processed. The CS component in the ROIC selectively samples photodetector values according to compressive sampling measurement matrices, compressing data in real-time during the read-out process rather than after complete image collection. This preliminary compression reduces the volume of data that needs to be processed and transmitted, thereby reducing bandwidth requirements while avoiding the complexity penalties of post-collection compression algorithms
Solution Approach 2:
The patent extracts only the essential information needed to represent the image by applying compressive sampling. Instead of collecting and then compressing all photodetector data, the system extracts a reduced set of linear combinations of photodetector values that capture the essential image information. This extraction approach reduces bandwidth requirements by transmitting only the compressed measurements rather than the full set of photodetector readings, while keeping device complexity manageable through hardware-based selective sampling
2Loss of information
If every photodetector in the FPA is sampled to capture the entire image, then image completeness is maintained, but bandwidth and power requirements increase
Solution Approach 1:
The patent applies parameter changes by transforming the sampling approach from uniform full-array sampling to selective compressive sampling. The system changes the sampling parameters by using compressive sampling measurement matrices that determine which photodetector values are sampled and how they are combined. This parameter change allows the system to capture complete image information through a reduced set of measurements, thereby reducing bandwidth requirements while maintaining image completeness through the mathematical properties of compressive sensing
3Measurement precision
If the photodetector array dimensions are increased to improve image resolution, then image quality improves, but power consumption and bandwidth requirements increase proportionally
Solution Approach 1:
The patent applies partial action by sampling only a subset of photodetector values according to compressive sampling measurement matrices rather than reading all photodetector data. The CS component selectively combines a portion of photodetector values to create compressed measurements that preserve image information. This partial sampling approach allows high-resolution imaging with larger photodetector arrays while reducing power consumption, as fewer photodetector channels need to be actively read out and processed
Data Source
AI summary
According to one aspect, a Read-Out Integrated Circuit (ROIC) with integrated Compressive Sampling (CS) is provided. The ROIC includes an input to couple to a photodetector array including a plurality of photodetectors and is configured to generate compressed image data by sampling and summing the values of the plurality of photodetectors consistent with a set of Compressive Sampling Measurement Matrices and provide the resulting coded aggregates to a signal processor as compressed image data.