Blood Cell Camera Autofocus Via Multi-Layer Focus Analysis
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Solution Overview
Problem
Existing blood cell analysis systems face challenges in capturing high-quality images due to focusing issues caused by temperature changes and the ineffectiveness of focusing methods on different types of blood cells, necessitating improvements in autofocusing and refocusing during image acquisition.
Innovation Solution
A system utilizing a camera and non-transitory computer readable medium with convolution filters and fully connected layers to automatically determine focusing quality, adjusting the camera position based on calculated focus distances to ensure in-focus images, employing convolutional neural networks trained on blood cell images to minimize regression errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional focusing methods are used, then the system can capture images, but image quality deteriorates due to temperature changes and different blood cell types
Solution Approach 1:
The system employs a feedback mechanism where the neural network continuously evaluates image sharpness and provides real-time feedback to adjust the focus position. The loss function calculates the gradient of image sharpness with respect to focus position, and this gradient feedback drives the optimization of focus position to maintain consistent image quality across temperature changes and different cell types.
Solution Approach 2:
The system dynamically changes the focus position parameter based on neural network predictions. Instead of using fixed focus settings, the system adjusts the focus position continuously by modifying this key parameter according to the neural network's evaluation of image sharpness, enabling adaptation to varying conditions.
2Measurement precision
If manual focusing adjustment is performed, then image quality can be improved, but the operation complexity and time consumption increase
Solution Approach 1:
The system performs self-focusing automatically without requiring manual intervention. The neural network independently evaluates image sharpness and determines the optimal focus position, enabling the system to service itself and eliminate the need for operator involvement in the focusing process.
Solution Approach 2:
The patent replaces manual mechanical focusing operations with an automated computational system. The neural network algorithm substitutes for the mechanical act of manual focus adjustment, using computational image sharpness evaluation and automated focus position determination to eliminate manual operation while maintaining or improving image quality.
3Adaptability or versatility
If focus position is fixed, then the system structure remains simple, but the system cannot adapt to temperature changes and different cell types
Solution Approach 1:
The neural network serves as an intermediary between the image capture system and the focus adjustment mechanism. It processes image data, evaluates sharpness, and translates this information into focus position adjustments, acting as a computational mediator that enables adaptability without requiring complex mechanical focusing systems.
Solution Approach 2:
The system introduces a computational dimension to the focusing process by using neural network algorithms. Instead of relying solely on mechanical dimensions and physical adjustments, the system adds the dimension of computational image analysis and automated control, enabling adaptability through software-based focus optimization.
Data Source
AI summary
A focusing distance (e.g., a distance between a focal plane of a camera used when n image was captured and the actual focal plane for an in-focus image) in a visual analysis system may be determined by subjecting one or more images captured by such a system to a multi-layer analysis. In such an analysis, an input may be subjected to one or more convolution filters, and the ultimate result of such convolution may be provided to a dense layer which can provide the focusing distance. This focusing distance may then be used to (re)focus a camera or for other purposes (e.g., generating an alert).