Multi-scale Image Definition Evaluation via Laplace Pyramid
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Solution Overview
Problem
Accurately determining the relative definition of microscopic images captured at different focal lengths is challenging due to their sensitivity to focal length changes, leading to misidentification of clear images, especially when distinguishing between captured subjects and non-captured objects like dust.
Innovation Solution
A method and apparatus that extract multi-scale features using a pyramid Laplace operator and a pre-trained relative definition scoring model to differentiate definition features, prioritizing the clarity of captured subjects over non-captured objects, thereby improving evaluation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-scale evaluation methods are used to assess image definition, then the evaluation process is simple, but the accuracy is insufficient especially when distinguishing captured subjects from non-captured objects like dust
Solution Approach 1:
The patent divides the image evaluation process into multiple scales by constructing Laplace pyramids with different decomposition levels. Each scale captures definition features at different object sizes, allowing the system to evaluate both fine details (captured subjects) and larger structures simultaneously, thereby improving evaluation accuracy without excessive complexity
Solution Approach 2:
The patent transitions from single-scale evaluation to multi-scale evaluation by adding the dimension of scale variation. By evaluating definition features across multiple Laplace pyramid levels, the system gains the ability to distinguish between objects of different sizes, effectively resolving the contradiction between simplicity and accuracy
2Adaptability or versatility
If focal length changes are made to capture different objects, then different objects can be focused on, but the definition of other objects changes significantly making it difficult to identify clear images
Solution Approach 1:
The patent employs a dynamic multi-scale evaluation approach where the Laplace pyramid decomposition level is adaptively selected based on the evaluation needs. This allows the system to dynamically adjust which scale is used for evaluation, making it adaptable to different imaging scenarios while maintaining high precision in identifying clear images regardless of focal length changes
Data Source
AI summary
Provided are a method and apparatus for evaluating image relative definition, a device and a medium, relating to technologies such as computer vision, deep learning and intelligent medical. A specific implementation solution is: extracting a multi-scale feature of each image in an image set, where the multi-scale feature is used for representing definition features of objects having different sizes in an image; and scoring relative definition of each image in the image set according to the multi-scale feature by using a relative definition scoring model pre-trained, where the purpose for training the relative definition scoring model is to learn a feature related to image definition in the multi-scale feature.