Super-Resolved Image Reconstruction via Iterative Structured Light
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Solution Overview
Problem
Conventional methods for obtaining super-resolved images using structured light require a large number of raw structured images, which slows down data acquisition speed, necessitating a method to improve spatial resolution with fewer images.
Innovation Solution
A method involving the acquisition of structured images, determination of modulation information including spatial frequency, phase shift, and modulation factor, and iterative processing to adjust structured patterns and sample images, resulting in a super-resolved image by continuing epochs until the difference between updated sample images is smaller than a predetermined value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of raw structured images are used to recover a super-resolved image, then the spatial resolution is improved, but the data acquisition speed deteriorates
Solution Approach 1:
The patent changes the parameter of the number of raw structured images from the conventional 9 or more to only 5 images. This parameter change enables the system to achieve super-resolved image recovery with fewer measurements, thereby improving data acquisition speed while maintaining spatial resolution through the iterative optimization algorithm that compensates for the reduced number of input images
2Productivity
If the number of raw structured images is reduced to improve data acquisition speed, then the productivity is improved, but the measurement precision deteriorates
Solution Approach 1:
The patent implements an iterative feedback mechanism where the algorithm repeatedly refines the super-resolved image by comparing the recovered image with the actual captured structured images. The feedback loop continues until convergence criteria are met, ensuring that spatial resolution is maintained even though only 5 raw images are used instead of the conventional 9 or more
Solution Approach 2:
The patent performs preliminary initialization of the super-resolved image and structured light parameters before the iterative refinement process. This preliminary action provides a starting point that guides the subsequent optimization iterations, enabling accurate resolution recovery with fewer input images by pre-establishing the computational framework
Data Source
AI summary
The present disclosure discloses a method, a data acquisition and image processing system and a non-transitory machine-readable medium for obtaining a super-resolved image of an object. The method comprises: obtaining a plurality of structured images of the object by structured light; determining, from the structured images, modulation information of each structured light that comprises spatial frequency, phase shift and modulation factor; initializing a sample image of the object according the structured images and initializing structured pattern of each structured light by the corresponding modulation information; and restoring the image with improved resolution by adjusting the sample image and the structured pattern iteratively.