Two-Pass Light-Field Reconstruction for 3D Sample Imaging
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Solution Overview
Problem
Existing methods for reconstructing object space from light-field images are inadequate, requiring iterative processes, noise filtering, and assumptions about optical configurations, leading to reconstruction errors and lack of automation.
Innovation Solution
A two-pass light-field reconstruction method that captures a light-field image, forward-projects it to z-planes in object space, and compares backward-projected images to select data for generating 2D or 3D object-space images, allowing for non-iterative and threshold-free processing without precise knowledge of optical geometry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative mapping and re-mapping methods are used for light-field reconstruction, then reconstruction can be performed with available data, but reconstruction errors are introduced and automation is prevented
Solution Approach 1:
The patent inverts the traditional reconstruction approach by using backward projection from object space to light-field space, then comparing with actual light-field data to iteratively refine the reconstruction. This inversion allows automated selection of optimal reconstruction parameters without manual intervention, resolving the contradiction between accuracy and automation.
Solution Approach 2:
The patent implements a feedback mechanism where the reconstructed object-space image is backward-projected to light-field space and compared with the original light-field image. This feedback loop enables automated error detection and correction, improving reconstruction accuracy while maintaining full automation without requiring manual inspection.
2Reliability
If filtering is applied to remove noise during reconstruction, then noise is reduced, but reconstruction errors are introduced
Solution Approach 1:
The patent converts the potentially harmful effect of noise into a beneficial signal by using the noise characteristics as part of the optimization criterion. The reconstruction algorithm optimizes to maximize correspondence between backward-projected and original light-field images, automatically distinguishing signal from noise without requiring manual filtering, thus avoiding filtering-induced errors while achieving noise reduction.
3Productivity
If assumptions about optical configuration are made, then reconstruction can proceed with incomplete information, but reconstruction errors are introduced
Solution Approach 1:
The patent enables the reconstruction system to self-determine optimal optical configuration parameters by optimizing correspondence between forward-projected and actual light-field images. The system automatically identifies the correct optical geometry without requiring manual assumptions or prior knowledge, achieving both rapid reconstruction and high accuracy simultaneously.
4Measurement precision
If user inspection is required to select candidate reconstructions, then reconstruction quality can be verified, but full automation is prevented
Solution Approach 1:
The patent implements automated feedback-based quality assessment by comparing backward-projected light-field images with the original captured light-field image. The optimization criterion automatically identifies the best reconstruction without requiring manual inspection, achieving both quality verification and full automation simultaneously through objective quantitative comparison.
Data Source
AI summary
Methods and systems for sample imaging via two-pass light-field reconstruction. In an exemplary method, a light-field image of a sample may be captured in a light-field plane. The light-field image may be forward-projected computationally to each of a plurality of z-planes in object space to generate a set of forward-projected z-plane images. Backward-projections computationally to the light-field plane of the same xy-region in object space from each z-plane image may be compared with the light-field image, to determine a respective degree of correspondence between the backward-projected xy-region from each of the z-plane images and the light-field image. For each different xy-region, at least one of the forward-projected z-plane images may be selected to contribute data for the different xy-region in a 2D or 3D object-space image of the sample.