Optical Fiber Image Reconstruction Using Unsorted Fiber Calibration
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Solution Overview
Problem
Conventional optical fibers with unsorted fibers lose spatial correlation of light, preventing effective image transmission, while image guides require high production effort and costs.
Innovation Solution
An iterative method for reconstructing an input image using an optical fiber with unsorted fibers and an image sensor, involving sequential calculations and replacements of brightness values based on sensor point measurements and weighting factors, to restore spatial correlation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If unsorted optical fibers are used for light transmission, then production costs and manufacturing effort are reduced, but spatial correlation of light is lost preventing image transmission
Solution Approach 1:
The patent applies preliminary action by capturing calibration data before actual image transmission. The system pre-characterizes the unsorted fiber bundle by measuring how light from each input position distributes across output sensor positions, storing this transmission matrix for later use in reconstructing images without requiring physical reconfiguration of fibers
Solution Approach 2:
The patent implements feedback through iterative reconstruction algorithms that use the captured calibration data to reconstruct input images from sensor measurements. The system continuously refines the reconstruction by comparing predicted sensor readings with actual measurements and adjusting the reconstructed image accordingly, enabling image recovery despite fiber disorganization
2Manufacturing precision
If image guides with sorted fiber arrangement are used, then spatial correlation and image transmission quality are maintained, but production costs and manufacturing complexity increase significantly
Solution Approach 1:
The patent uses copying by creating a digital model (transmission matrix) of the fiber bundle's light transmission characteristics instead of physically sorting the fibers. This digital copy enables image reconstruction through computational processing, replacing the need for precise physical fiber arrangement while achieving similar image transmission functionality
Solution Approach 2:
The patent replaces the mechanical sorting system with a computational approach. Instead of physically arranging fibers in sorted order during manufacturing, the system uses calibration measurements and iterative algorithms to achieve image reconstruction, substituting mechanical precision with computational processing
3Measurement precision
If iterative reconstruction calculations are performed for all sensor points and input image areas, then reconstruction accuracy is improved, but computational effort and time increase
Solution Approach 1:
The patent applies partial action by selectively processing only the most significant contributions to image reconstruction. The iterative algorithm focuses computational effort on the most influential sensor points and input image areas, stopping when a satisfactory reconstruction is achieved rather than exhaustively processing all possible combinations
Solution Approach 2:
The patent uses preliminary action by pre-computing and storing the transmission matrix during a calibration phase. This pre-characterization of the fiber bundle's light distribution patterns enables faster real-time reconstruction by avoiding repeated full calculations, as the system only needs to apply the pre-computed transmission characteristics to new images
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
Reconstructs input images accurately and efficiently from output images captured by unsorted optical fibers, reducing computational effort and maintaining image quality.
Implementation Method 1
Light transmission typically occurs through internal reflection of the light at an optical interface on the fiber's cladding
Data Source
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Figure 1B
Figure 1C
AI summary
A method (200; 400) for iteratively reconstructing an input image (E) from a captured output image (A), wherein the output image (A) is generated at least partially by transmitting components of the input image (E) by means of an optical fiber (110; 310) comprising at least partially unsorted fibers (112) and by means of an image sensor (130;330), which comprises a plurality of sensor points (134), comprises a calculation (210) of an input image area brightness value (Hi') for a first area (EG-i) of the input image (E) at least partially based on at least one first sensor point brightness value (Mj) assigned to a first sensor point (134-j) of the image sensor (130) and indicating a detected brightness of the output image (A) in the area of the first sensor point (134-j), a first weighting factor (wij) assigned to the first area (EG-i) of the input image (E) with respect to the first sensor point (134-j), and at least one further input image area brightness value (Hk) assigned to a further area (EG-k) of the input image (E) and which is combined with a further weighting factor (wkj) assigned to the further area (EG-k) of the input image (E) with respect to the first The sensor point (134-j) is assigned and weighted. The procedure (200;400) also includes replacing (220) a first input image area brightness value (Hi) assigned to the first area (EG-i) of the input image (E) with the calculated input image area brightness value (Hi') for use as the henceforth first input image area brightness value (Hi). The aforementioned procedural steps of calculating (210) and replacing (220) are applied sequentially, in particular iteratively (230; 430).