Deep Neural Network Microscopy Image Super-Resolution
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Solution Overview
Problem
Current microscopy techniques face challenges in enhancing image resolution, depth-of-field, and signal-to-noise ratio without significant computational cost or hardware modifications, particularly in bright-field and fluorescence microscopy.
Innovation Solution
A deep neural network, specifically a convolutional neural network, is trained using co-registered training images to improve the resolution, depth-of-field, and signal-to-noise ratio of microscopy images by learning statistical transformations between low-resolution and high-resolution images, enabling the enhancement of images from various microscopy modalities without requiring precise modeling of the imaging process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computational super-resolution microscopy techniques use accurate numerical models to simulate the imaging process, then image quality is improved, but computational cost and parameter search complexity increase
Solution Approach 1:
The patent replaces traditional numerical models and physics-based simulations with a deep learning-based computational framework. Instead of using complex mathematical models to simulate light propagation and image formation, the system trains a neural network to learn the mapping from low-resolution to high-resolution images directly from data, substituting mechanical/physical modeling with a data-driven approach that reduces computational burden while maintaining or improving image quality
Solution Approach 2:
The patent changes the fundamental parameters of the computational approach by transitioning from model-based parameters (point spread function, optical transfer function) to data-driven parameters learned during neural network training. This allows the system to achieve high image quality without exhaustive parameter search, as the network automatically learns optimal transformation parameters from training data
2Measurement precision
If deep learning techniques are applied to microscopy image enhancement, then image resolution and signal-to-noise ratio are improved, but training data requirements and model complexity increase
Solution Approach 1:
The patent creates a universal deep learning model that can be applied across multiple microscopy modalities and imaging conditions. The trained network serves multiple functions: enhancing resolution, improving signal-to-noise ratio, and working across different staining types and tissue samples. This universality reduces the need for extensive modality-specific training data, as the model learns generalizable features that transfer across different imaging scenarios
Solution Approach 2:
The patent performs preliminary training of the deep learning model using curated training datasets before deployment. By pre-training the network on diverse microscopy images during an offline phase, the system prepares the model with learned features and transformations that can then be applied to new images without requiring additional training data at the time of use, thus reducing real-time data requirements
Data Source
AI summary
A microscopy method includes a trained deep neural network that is executed by software using one or more processors of a computing device, the trained deep neural network trained with a training set of images comprising co-registered pairs of high-resolution microscopy images or image patches of a sample and their corresponding low-resolution microscopy images or image patches of the same sample. A microscopy input image of a sample to be imaged is input to the trained deep neural network which rapidly outputs an output image of the sample, the output image having improved one or more of spatial resolution, depth-of-field, signal-to-noise ratio, and/or image contrast.