Compressive Sensing Optic Fiber Bundle for High Resolution Imaging
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Solution Overview
Problem
Image resolution in tightly constrained spaces, such as those accessed by borescopes, endoscopes, and videoscopes, is limited due to spatial constraints, necessitating innovative solutions for capturing high-resolution images with limited access.
Innovation Solution
A compressive sensing optic system comprising multiple compressive sensing elements and a fiber optic bundle that integrates random optical samples to produce compressed optical samples, which are then transmitted to a sensor for image reconstruction, allowing for higher resolution images from fewer measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional imaging systems are used in tightly constrained spaces, then the system can capture images, but the image resolution is limited due to spatial constraints
Solution Approach 1:
The imaging system is segmented into multiple compressive sensing elements (CSEs) that collectively capture the scene. Each CSE captures a portion of the scene, and their combined measurements enable high-resolution reconstruction without requiring each individual element to be large, thus resolving the contradiction between image resolution and system size in constrained spaces.
Solution Approach 2:
The system transitions from direct spatial mapping to a transformed measurement domain through compressive sensing. By capturing random projections of the scene in a different dimensional space and using computational reconstruction, the system achieves high resolution with fewer, smaller sensing elements, effectively moving the problem from spatial constraints to computational processing.
2Volume of moving object
If the number of optical samples is reduced to fit constrained spaces, then the system size decreases, but traditionally this would reduce image quality
Solution Approach 1:
Computational reconstruction algorithms serve as an intermediary between the reduced set of optical samples and the final high-resolution image. This intermediary processing step enables the system to recover detailed image information from fewer measurements than traditionally required, allowing smaller system size without sacrificing image resolution.
Solution Approach 2:
The system changes the measurement parameters by using random projections instead of direct spatial sampling. This parameter change in the measurement domain allows the system to capture sufficient information with fewer samples, enabling reduced system size while maintaining or improving image resolution through efficient use of measurement information.
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
Enables the capture of higher resolution images in constrained spaces by reducing the number of required optical samples, facilitating the use of smaller imaging systems while maintaining image quality.
Implementation Method 1
Each FOE is configured to integrate one or more accepted scene optical samples to produce an associated compressed optical sample
Implementation Method 2
Each FOE is configured to carry the associated compressed optical sample from the first end to a second end of the FOB configured to be coupled to a sensor
Data Source
AI summary
The present disclosure relates to a compressive sensing imaging system which may include a compressive sensing optic (CSO) that includes a plurality of compressive sensing elements (CSEs), a fiber optic bundle (FOB) that includes a plurality of fiber optic elements (FOEs) and a sensor that includes a plurality of optical sensing elements (OSEs). Each CSE is configured to capture a respective random CSE optical sample related to a respective portion of a scene and to provide the respective CSE optical sample to the FOB. Each FOE is configured to integrate one or more accepted scene optical samples to produce an associated compressed optical sample and each scene optical sample corresponds to at least a portion of a respective CSE optical sample. Each FOE is further configured to provide the associated compressed optical sample to the sensor. Each OSE is configured to integrate one or more received sensor optical samples.