Distributed Quantum Imaging with Compressed Sensing
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Solution Overview
Problem
Traditional quantum imaging technologies face challenges with high sampling rates, long data processing times, and low resolution due to the need for large amounts of sampling data, which is inefficient and resource-intensive.
Innovation Solution
A distributed quantum imaging system utilizing a compressed sensing algorithm with a sensing matrix constructed from light field information and measurement electrical signals, allowing for efficient data sampling and compression, and improving image recovery through a spatially distributed laser light source and spatial light modulators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional optical correlation calculation imaging algorithm is used, then image recovery can be performed, but large amount of sampling data is required, taking long time and resulting in low resolution
Solution Approach 1:
The patent changes the fundamental parameter of the imaging algorithm from traditional optical correlation calculation to compressed sensing algorithm. This parameter change enables image recovery with significantly reduced sampling data volume while improving resolution, as compressed sensing exploits the sparsity of images in certain bases to reconstruct high-quality images from fewer measurements
Solution Approach 2:
The patent replaces the mechanical sampling process governed by Nyquist-Shannon theorem with a compressed sensing-based sampling approach. Instead of requiring sampling at twice the maximum frequency, the system uses randomized sampling combined with sparse representation and optimization algorithms to achieve accurate image reconstruction with far fewer samples
2Measurement precision
If traditional optical correlation calculation imaging algorithm is used, then image recovery can be performed, but long data processing time is required
Solution Approach 1:
The patent changes the computational parameter from correlation-based optimization to compressed sensing optimization. The compressed sensing framework uses efficient algorithms like orthogonal matching pursuit or iterative thresholding that are computationally less intensive than traditional correlation calculation, especially when dealing with large datasets, thus reducing processing time while maintaining or improving recovery accuracy
Solution Approach 2:
The patent extracts and utilizes the sparsity property of images in specific bases (such as wavelet or Fourier bases) to separate the essential image information from redundant data. By representing the image as a sparse vector in a transformed domain, the system can recover the image from fewer measurements and process data more efficiently
3Reliability
If Nyquist-Shannon sampling theorem is followed, then distortion-free reconstruction is guaranteed, but increasing pressure on signal during sampling, transmission, and storage
Solution Approach 1:
The patent changes the sampling parameter from Nyquist rate (twice the maximum frequency) to a much lower rate enabled by compressed sensing. By exploiting the sparsity of the signal in a suitable basis and using randomized sampling combined with optimization algorithms, the system achieves distortion-free reconstruction with significantly fewer samples, thereby reducing the energy and resources required for sampling, transmission, and storage
Solution Approach 2:
The patent applies partial sampling instead of complete Nyquist-rate sampling. By randomly selecting a subset of measurements and using the sparsity constraint to recover the full signal, the system achieves reliable reconstruction with only a fraction of the samples required by traditional methods, reducing overall resource consumption
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 reduces errors and improves image resolution and quality by using a smaller amount of sampling data, enhancing the efficiency of quantum imaging and achieving better image recovery compared to traditional methods.
Implementation Method 1
each spatial light modulator is configured to modulate a light field parameter generated by a corresponding laser device during in measurement process, and project a modulated light signal onto an object to be measured
Implementation Method 2
the detector is configured to collect transmitted light obtained in response to a light signal outputted from each laser device passing through the object to be measured, convert the transmitted light into a corresponding measurement electrical signal
Data Source
AI summary
Disclosed are a distributed quantum imaging method, apparatus and system, and a computer-readable storage medium. The distributed quantum imaging system comprises a plurality of laser devices that are placed at different spatial positions, a plurality of spatial light modulators, a detector and an imaging processor, wherein each laser device uniquely corresponds to one spatial light modulator. Each spatial light modulator is used for modulating a light field parameter generated by a corresponding laser device during each measurement process, and projecting a modulated light signal onto an object to be measured; the detector is used for collecting transmitted light obtained after an output light signal of each laser device passes through said object, converting the transmitted light into a corresponding measurement electrical signal and sending the measurement electrical signal to the imaging processor; and the imaging processor is used for performing reconstruction by using a compressed sensing algorithm, a sensing matrix that is constructed on the basis of light field information during a plurality of measurement processes, and the measurement electrical signal, so as to obtain information of said object. By means of the present application, the quantum imaging efficiency and the quantum imaging resolution can be effectively improved.