Lensless Optical Image Restoration System for Resolution
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Solution Overview
Problem
Current optical imaging platforms are limited by complexity, high cost, and require trained personnel, making them unsuitable for widespread application, especially in remote and resource-constrained regions, and face challenges with insufficient data for effective deep learning models, leading to overfitting issues.
Innovation Solution
A lensless optical image restoration system using a deep learning algorithm that includes a light source, pinhole, testing platform, and image sensor, with an image processing device for image reconstruction and automatic recognition, employing data augmentation techniques like translation, rotation, and inversion to enhance model generalization without the need for an optical lens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional optical microscope is used, then image quality and resolution are good, but the system complexity and cost are high
Solution Approach 1:
The patent extracts and removes the optical lens from the traditional microscope system, retaining only the essential components (light source, pinhole, image sensor). This simplification eliminates complex optical pathways while preserving the core imaging function through computational reconstruction.
Solution Approach 2:
The patent replaces the mechanical optical lens system with a computational image restoration system based on deep learning algorithms. The physical optical reconstruction is substituted by digital signal processing and neural network-based image recovery, achieving comparable resolution with simpler hardware.
2Measurement precision
If a conventional optical microscope is used, then image quality is good, but the cost and operational requirements are high
Solution Approach 1:
The patent employs inexpensive, easily manufactured components such as a simple pinhole aperture and standard image sensor instead of expensive precision optical lenses and complex mechanical assemblies. This approach prioritizes cost-effective implementation while achieving functional equivalence through computational methods.
3Loss of time
If deep learning is applied with insufficient training data, then model training is fast, but prediction accuracy drops due to overfitting
Solution Approach 1:
The patent performs data augmentation and preprocessing operations before training the deep learning model. By pre-processing the training data to create diverse augmented samples, the model learns more robust features from limited data, preventing overfitting and improving generalization to unseen data.
4Reliability
If data augmentation is applied, then model generalization improves, but data processing complexity increases
Solution Approach 1:
The patent creates augmented training data by generating transformed copies of original images (rotations, flips, translations). These synthetic copies expand the training dataset without requiring additional physical samples, improving model generalization while avoiding the complexity of collecting and processing diverse real-world data.
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 system achieves image reconstruction with resolution and recognition comparable to traditional optical microscopes, offering a cost-effective, automated, and efficient solution for image processing, enabling miniaturization and a large image field of view.
Implementation Method 1
The image sensor is disposed below the testing platform, and used to sense the testing sample so as to output an optical diffraction signal
Data Source
AI summary
Disclosed is an optical system using image restoration, including a light source, a pinhole, a testing platform, an image sensor and an image processing device. The pinhole is disposed on a light transmission path of the light source. The testing platform is disposed on the light transmission path of the light source and the pinhole is located between the light source and the testing platform. The testing platform is used to place a testing sample. The image sensor is disposed below the testing platform, and used to sense the testing sample so as to output an optical diffraction signal. The image processing device is electrically connected to the image sensor and used to perform signal processing and optical signal recognition on the optical diffraction signal of the testing sample so as to obtain a clear image of the testing sample.