Compressive Sensing Reconstructs High-Resolution Thermal Images
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Solution Overview
Problem
High-resolution imaging beyond the visible electromagnetic range is challenging due to the difficulty and expense of producing silicon-based sensors sensitive in thermal bands, as silicon is not compatible with thermal photo-sensitive materials, limiting the use of low-resolution sensor chips in thermal imaging applications.
Innovation Solution
The method involves configuring a digital light modulator with a spatially varying pattern, collecting and focusing light energy on low-resolution photodetectors, and using compressive sensing techniques to reconstruct high-resolution images by exploiting joint sparsity assumptions, allowing for the combination of data from multiple detectors to form a high-resolution image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If high-resolution thermal imaging sensors are produced using silicon-based manufacturing techniques, then manufacturing cost and complexity increase significantly, but the compatibility with thermal photo-sensitive materials remains poor
Solution Approach 1:
The patent uses compressive sensing to create a computational copy of the high-resolution image from low-resolution sensor data. Instead of manufacturing a physical high-resolution thermal sensor that is difficult to produce, the system captures low-resolution thermal data and generates a high-resolution representation through algorithms that exploit joint sparsity assumptions, effectively copying the high-resolution image from degraded measurements.
Solution Approach 2:
The patent replaces the mechanical/optical approach of using physical high-resolution thermal sensors with a computational method. Instead of relying on the physical properties of thermal-sensitive materials being manufactured at high resolution, the system uses signal processing and compressive sensing algorithms to reconstruct the high-resolution image from lower-resolution measurements, substituting physical manufacturing challenges with computational solutions.
2Measurement precision
If high-resolution imaging is achieved using traditional high-resolution sensors, then image quality improves, but manufacturing cost and complexity increase
Solution Approach 1:
The patent employs low-resolution sensors that are cheaper and easier to manufacture, accepting that individual sensor elements are lower quality but gaining overall system performance through computational methods. The low-resolution sensors serve as disposable measurement elements that, when combined through compressive sensing, produce high-resolution output, effectively replacing expensive high-resolution sensors with multiple cheap low-resolution ones.
Solution Approach 2:
The patent merges data from multiple low-resolution sensor measurements to create a single high-resolution image. By combining information from multiple degraded measurements and exploiting joint sparsity across different channels or time frames, the system reconstructs a high-resolution image that would be impossible to obtain from a single low-resolution sensor alone, achieving high measurement precision through aggregation of lower-quality data.
3Ease of manufacture
If compressive sensing is used to reconstruct images from low-resolution sensors, then manufacturing cost decreases, but computational complexity increases
Solution Approach 1:
The patent applies compressive sensing by taking fewer measurements than the number of pixels in the final image, using random projection matrices to capture essential information efficiently. Instead of capturing every pixel value directly (excessive action), the system takes a subset of measurements (partial action) that still allows for accurate reconstruction through sparse representation, reducing the number of sensor elements needed while maintaining image quality.
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 high-resolution imaging using low-resolution sensors, reducing production costs and overcoming compatibility issues with thermal sensors, while achieving high-definition imaging across various electromagnetic ranges.
Implementation Method 1
configuring a digital light modulator according to a spatially varying pattern
Implementation Method 2
collecting and optically focusing light energy associated with the scene incident on the spatially varying pattern on each of at least two photodetectors
Data Source
AI summary
A method and system for reconstructing an image of a scene comprises configuring a digital light modulator according to a spatially varying pattern. Light energy associated with the scene and incident on the spatially varying pattern is collected and optically focused on the photodetectors. Data indicative of the intensity of the focused light energy from each of said at least two photodetectors is collected. Data from the photodetectors is then combined to reconstruct an image of the scene.