Deep Learning Super-Resolution Imaging from Single Wide-Field Images
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Solution Overview
Problem
The resolution limit of existing optical microscopes, constrained by the optical diffraction limit, hinders the improvement of gene sequencing throughput and increases costs, necessitating a method to enhance spatial resolution without additional time or hardware.
Innovation Solution
A deep learning-based super-resolution analysis system that constructs super-resolution images from wide-field images using a trained model, compatible with existing imaging systems, reducing the need for multiple image acquisitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional optical microscopy is used for gene sequencing, then the system is simple and cost-effective, but the resolution is limited by the optical diffraction limit and cannot be improved further
Solution Approach 1:
The patent replaces traditional optical mechanical systems with a computational approach. Instead of using complex optical components to achieve super-resolution, the system uses a deep learning model (super-resolution realization model) that has been trained on paired low-resolution and high-resolution images. The model takes wide-field low-resolution images as input and outputs corresponding super-resolution images, substituting the mechanical/optical resolution enhancement with an information-processing approach.
Solution Approach 2:
The patent creates a computational copy of the high-resolution image from the low-resolution image through the trained deep learning model. The model learns the mapping relationship between low-resolution and high-resolution images during training, then applies this learned transformation to generate super-resolution images from new wide-field images, effectively copying the high-resolution structure from the low-resolution input.
2Measurement precision
If structured illumination super-resolution fluorescence microscopy is used, then the resolution can be improved by about two times, but the core technology of high-density stripe structured illumination and fast image reconstruction algorithm is not yet broken through
Solution Approach 1:
The patent replaces the complex structured illumination optical system with a computational model. Instead of physically implementing high-density stripe structured illumination and complex image reconstruction algorithms, the system uses a pre-trained deep learning model that encapsulates the super-resolution transformation. This substitutes the unready core technology with a computational approach that achieves similar or better resolution without the technical hurdles.
Solution Approach 2:
The patent uses a software-based solution (deep learning model) that can be easily implemented and updated compared to hardware-based structured illumination systems. The model can be trained on existing data and applied without requiring complex optical modifications, making it a more practical and readily deployable solution for achieving super-resolution in gene sequencing.
3Measurement precision
If multiple image acquisitions are performed to achieve super-resolution, then the resolution is improved, but the image acquisition time increases
Solution Approach 1:
The patent performs the super-resolution transformation in advance during the image acquisition process. Instead of requiring multiple separate image acquisitions and then performing reconstruction, the system acquires a single wide-field image and then applies the pre-trained deep learning model to generate the super-resolution image. This preliminary preparation of the computational model eliminates the need for time-consuming multi-image acquisition and reconstruction processes.
Data Source
AI summary
The present application pertains to the technical field of image processing and artificial intelligence, and discloses a deep learning-based super-resolution analysis system and method. The super-resolution analysis system includes an analysis unit, which includes a super-resolution realization model and can be executed by a processor for constructing a super-resolution image based on an input wide-field image; wherein the super-resolution realization model is a trained deep learning model, and a training dataset for training the super-resolution realization model and the input wide-field image originate from one and the same imaging module. The present application further discloses a corresponding imaging device and model training method. According to the present application, with only the need of reconstructing a wide-field image, a super-resolution image based on a mapping relationship can be output via a deep learning algorithm, thus reducing the number of images to get acquired, achieving improved resolution without additionally time increase.