DIC Phase Reconstruction With Pix2pix Networks for Artifact Reduction
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Solution Overview
Problem
DIC microscopy faces challenges in accurately reconstructing quantitative phase images due to nonlinear relationships between intensity images and optical path length gradients, leading to directional artifacts and noise accumulation, especially when acquiring images with different shearing directions requires sample rotation, which can introduce misalignment and defocus.
Innovation Solution
A phase reconstruction method using a pix2pix network, involving an end-to-end deep learning strategy with a generator and discriminator, trained on digital holographic phase-shift interferograms of Hela cells and polystyrene microspheres, employing an adversarial loss and similarity loss to optimize network parameters for accurate phase reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional integration methods are used for phase reconstruction from DIC images, then the reconstruction process is straightforward, but directional artifacts and noise accumulation occur due to nonlinear relationships between intensity images and optical path length gradients
Solution Approach 1:
The patent replaces traditional mechanical integration methods with a deep learning-based pix2pix network. The neural network learns the nonlinear mapping from differential phases to ground-truth phases, substituting the mathematical integration process with a data-driven approach that avoids accumulation errors and directional artifacts inherent in traditional methods.
Solution Approach 2:
The patent transforms the phase reconstruction problem from a deterministic mathematical integration task into a probabilistic learning task. By changing from fixed integration algorithms to a trained neural network model, the system adapts to nonlinear relationships and eliminates artifacts through learned transformations rather than rigid mathematical operations.
2Measurement precision
If images with different shearing directions are acquired to improve reconstruction quality, then phase reconstruction accuracy improves, but sample rotation is required which introduces misalignment and defocus
Solution Approach 1:
The patent extracts and eliminates the need for physical sample rotation by using a single shearing direction. The pix2pix network is trained to compensate for the limitations of single-direction shearing, extracting only the necessary differential phase information without requiring multiple rotational acquisitions, thereby avoiding misalignment and defocus issues.
Solution Approach 2:
Instead of acquiring multiple images with different shearing directions and then integrating them (the conventional approach), the patent inverts the strategy by acquiring a single set of images and using a neural network to directly reconstruct the phase, reversing the cause-effect relationship and eliminating the need for sample rotation.
3Measurement precision
If a pix2pix network is used for phase reconstruction, then artifact-free and dynamic phase reconstruction with high accuracy is achieved, but the system complexity increases due to deep learning model training and deployment
Solution Approach 1:
The patent performs the complex training and model development work in advance (preliminary action). The pix2pix network is trained offline on a large dataset of DIC images and corresponding ground-truth phases, and the trained model is then deployed for rapid inference. This separates the complex learning phase from the operational phase, reducing the apparent complexity during actual use.
Solution Approach 2:
The patent uses a trained neural network model (a digital copy of the learned knowledge) to perform phase reconstruction without requiring the physical presence or re-execution of the training process. The model copies the learned transformations and applies them to new images, simplifying the operational system while maintaining high accuracy.
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 achieves artifact-free and dynamic phase reconstruction with high accuracy, enhancing the realism and stability of DIC images by quantitatively recovering differential phases from target DIC images, improving the fidelity and robustness of phase reconstruction.
Implementation Method 1
The DIC microscopy is based on shear interference of coherent light and indirectly records physical properties of a sample by mapping gradients of the optical path length to the image intensity
Data Source
AI summary
The present disclosure of some embodiments provide a phase reconstruction method for differential interference contrast microscopy based on a pix2pix network, the method comprising the following steps: S1, constructing an end-to-end deep learning strategy based on the pix2pix network; S2, collecting and constructing dataset; S3, Training the pix2pix network; S4, analyzing network accuracy and convergence, and recording error curves of the datasets; S5, analyzing network performance based on error curves of the training set and the test set; S6, performing quantitative phase reconstruction for differential interference contrast microscopy using the trained pix2pix network.