Magnification Factor Estimation for Blurred Images
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Solution Overview
Problem
Existing methods for determining the magnification factor of an image after a magnification process are inaccurate, leading to excessive or insufficient edge enhancement due to blurring, especially when block noise is present.
Innovation Solution
A magnification factor estimation device that calculates high and spatial frequency component characteristic values, removes high-frequency components, and determines block noise to accurately estimate the magnification factor, adjusting edge extraction threshold values and thinning factors accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If resolution determination is performed by counting pixels in blurred images, then the process is simple, but the measurement precision deteriorates
Solution Approach 1:
The patent applies preliminary action by performing edge enhancement processing on the image before determining resolution. The edge enhancement is performed based on estimated magnification factors, which prepares the image by reducing blur effects prior to the resolution measurement step, thereby improving measurement precision without complicating the overall process
Solution Approach 2:
The patent replaces the simple pixel-counting mechanical method with a more sophisticated approach that uses edge enhancement processing and magnification factor estimation. This substitution involves using spatial frequency analysis and iterative optimization algorithms to achieve accurate resolution determination in blurred images, trading computational complexity for measurement precision
2Device complexity
If edge enhancement is performed on blurred images without accurate magnification estimation, then the process can be simplified, but manufacturing precision deteriorates due to excessive or insufficient enhancement
Solution Approach 1:
The patent applies dynamics by making the edge enhancement process adaptive rather than static. The enhancement strength is dynamically adjusted based on the estimated magnification factor, which itself is determined through iterative optimization. This dynamic approach allows the system to automatically adapt to different levels of blur and magnification, achieving precise edge enhancement without requiring manual parameter tuning
Solution Approach 2:
The patent implements feedback by using the estimated magnification factor to control the edge enhancement process. The system estimates the magnification factor from the blurred image, uses this estimate to guide the edge enhancement processing, and iteratively refines both the magnification estimation and enhancement parameters. This closed-loop feedback mechanism ensures that edge enhancement precision is maintained across varying image conditions
3Device complexity
If simple pixel counting is used for resolution determination, then the device complexity is low, but measurement precision deteriorates in magnified and blurred images
Solution Approach 1:
The patent applies preliminary action by performing magnification factor estimation and edge enhancement before final resolution determination. This preliminary processing prepares the blurred image by reducing blur effects and estimating key parameters, enabling subsequent resolution measurement to be performed with higher accuracy even though the overall device complexity increases
Solution Approach 2:
The patent applies parameter changes by transforming the resolution determination problem into a magnification factor estimation problem. Instead of directly counting pixels in the blurred image, the system estimates the magnification factor that produced the blur, then uses this parameter to guide edge enhancement and final resolution measurement. This parameter transformation approach maintains measurement precision while managing device complexity through algorithmic optimization
Data Source
AI summary
A magnification factor is estimated for a magnified image. A generating means generates an image by removing a high frequency component from an input image. A first calculating means calculates a high frequency component greater than or equal to a prescribed frequency component in the spatial frequency component of the input image, as a spatial frequency component characteristic value. A second calculating means calculates the spatial frequency component of the input image, as a second spatial frequency component characteristic value. A determining means determines whether each pixel is block noise. A magnification factor estimating means removes pixels determined to be block noise by the determining means from summation target pixels and, estimates the magnification factor of the input image to be higher when the difference between the first spatial frequency component characteristic values and second spatial frequency component characteristic values of the remaining pixels is smaller.