Iterative Image Reconstruction for Unsorted Fiber Light Guides
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing light guides with unsorted fibers lose local correlation of light, preventing optical image transmission, while image guides require high production effort and costs, making them unsuitable for applications needing moderate optical quality.
Innovation Solution
An iterative method reconstructs an input image from an output image using an image sensor with sensor points, calculating and replacing brightness values based on weighting factors to restore local correlation, even with unsorted fibers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image guides with sorted fibers are used for optical image transmission, then image quality and local correlation are improved, but production effort and costs increase considerably
Solution Approach 1:
Instead of sorting fibers to preserve local correlation, the patent inverts the approach by using unsorted fibers and recovering local correlation through computational reconstruction of the image from sensor data, thereby avoiding the complex and costly fiber sorting process while achieving the same functional outcome
Solution Approach 2:
The patent replaces the mechanical/optical system of sorted fiber arrangements with a computational system that uses algorithms to reconstruct images from unsorted fiber output, substituting physical fiber precision with digital signal processing
2Manufacturing precision
If image guides with sorted fibers are used, then optical quality including resolution and sharpness is improved, but production costs increase considerably
Solution Approach 1:
The patent uses inexpensive unsorted fiber bundles instead of expensive sorted image guides, accepting that the fibers will scramble light patterns but relying on computational methods to recover the image, thereby achieving acceptable optical quality at much lower cost
Solution Approach 2:
The patent changes the fundamental parameter from physical fiber arrangement (sorted) to computational processing (reconstruction algorithms), allowing the system to achieve image quality through software rather than expensive hardware precision
3Ease of manufacture
If light guides with unsorted fibers are used, then production costs are reduced, but local correlation is lost preventing image transmission
Solution Approach 1:
The patent introduces feedback through iterative reconstruction algorithms that continuously refine the recovered image by comparing sensor measurements with model predictions, gradually recovering local correlation information that was lost in the unsorted fiber transmission
Solution Approach 2:
The patent introduces computational reconstruction algorithms as an intermediary between the unsorted fiber output and the final image, mediating the transformation of scrambled light patterns back into coherent image 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
The method effectively reconstructs input images from output images with unsorted fibers, achieving moderate optical quality at reduced production costs, suitable for applications where direct image acquisition is difficult.
Implementation Method 1
The light is typically guided by internal reflection of the light in the fiber at an optical interface in the area of the fiber's cladding surface.
Data Source
AI summary
A method for iteratively reconstructing an input image from an acquired output image generated by transmitting components of the input image by a light guide having unsorted fibers where the output image is detected by an image sensor having a plurality of sensor points includes the steps of: calculating an input image area brightness value for a first area of the input image based on at least a first sensor point brightness value, a first weighting factor, and at least one further input image area brightness; and replacing a first input image area brightness value with the calculated input image area brightness value for subsequent use as the first input image area brightness value. The calculating and replacing are sequentially performed.