Image Alignment Conversion Coefficient Recalculation for Focus Stacking
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Solution Overview
Problem
In image processing, especially when capturing images of objects at varying distances, the shallow depth of field often results in only a part of the object being in focus, making it difficult to obtain accurate alignment for focus stacking, especially when the movement of feature points is small, leading to challenges in calculating a reliable conversion coefficient for image alignment.
Innovation Solution
An image processing apparatus that detects feature points from multiple images, calculates a conversion coefficient for alignment using these points, and performs combining processing to generate a combined image where the entire imaging area is in focus, by using a system control unit to determine when the conversion coefficient between images does not meet a predetermined condition and recalculating based on more distant in-focus positions to enhance alignment accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a large number of images are captured while finely changing the in-focus position to improve combined image quality, then the quality of the focus stacked image is improved, but the movement amount of the feature point becomes smaller than a threshold value, making it difficult to obtain accurate conversion coefficient
Solution Approach 1:
The system performs preliminary evaluation of conversion coefficient accuracy before proceeding with image combining. By calculating the accuracy of the conversion coefficient in advance based on feature point movement amount, the system can determine whether the coefficient is reliable for alignment, preventing subsequent alignment errors while maintaining fine in-focus position changes for high-quality focus stacking
Solution Approach 2:
The system implements a feedback mechanism where the accuracy of the conversion coefficient is evaluated based on the movement amount of feature points. When the movement amount is insufficient (below threshold), the system identifies this as inaccurate and triggers recalculation using alternative image pairs, ensuring that only accurate conversion coefficients are used for the final image combining process
2Reliability
If the amount of change of the in-focus position is made small to capture more images for better focus stacking, then the depth of field coverage is improved, but the movement amount of the feature point becomes smaller than a threshold value, which may make it difficult to obtain the accurate conversion coefficient
Solution Approach 1:
The system performs preliminary evaluation of conversion coefficient accuracy before proceeding with image combining. By calculating the accuracy of the conversion coefficient in advance based on feature point movement amount, the system can determine whether the coefficient is reliable for alignment, preventing subsequent alignment errors while maintaining fine in-focus position changes for high-quality focus stacking
Solution Approach 2:
When the conversion coefficient accuracy is insufficient due to small feature point movement, the system performs excessive action by recalculating the conversion coefficient using alternative image pairs (images with larger in-focus position differences). This partial recalculation ensures that at least some accurate conversion coefficients are obtained, even if it requires additional processing beyond the original plan
Data Source
AI summary
To enhance the accuracy of a conversion coefficient detected from a plurality of images captured while finely moving an in-focus position, an image processing apparatus includes a detection unit configured to detect feature points from a plurality of images having different in-focus positions, and a combining unit configured to calculate a conversion coefficient for alignment using the feature points and perform combining processing based on the conversion coefficient. The combining unit calculates a conversion coefficient of a second image in the plurality of images with respect to a first image in the plurality of images based on a conversion coefficient calculated using the feature points detected from the first image and a third image in the plurality of images.