High-precision image registration method based on SIFT feature extraction and PROSAC optimization

Abstract

The invention describes a high-precision image registration method based on SIFT feature extraction and PROSAC optimization in detail, and aims to improve the precision and robustness of image registration in a complex industrial environment. The method comprises the steps of image preprocessing, SIFT feature extraction and enhancement, FLANN adaptive matching optimization, robust geometric verification, intelligent image fusion and the like. Wherein in the feature matching stage, a KD tree index is constructed, and an improved Low's dynamic threshold ratio test mechanism and a bidirectional verification mechanism are combined, so that the mismatching rate is remarkably reduced; according to the robust geometric verification, an improved PROSAC algorithm is adopted to estimate a homography matrix between images, and the robustness to abnormal values is improved; according to the intelligent image fusion, image fusion and output are completed by adopting multi-frequency fusion and histogram specification. Experiments show that the method achieves 98.2% of registration accuracy on an industrial data set, the registration accuracy is improved by 23.5% compared with a traditional SIFT + RANSAC scheme, the single-frame processing time is shorter than 200 ms, the mismatching rate is reduced to 3% or below, and the real-time detection requirement of a high-speed production line can be met. The method has high adaptability, high robustness and high precision, is suitable for industrial image registration and defect detection under various complex working conditions, and provides powerful technical support for quality control of industrial products.

Claims

1. A high-precision image registration method based on SIFT feature extraction and PROSAC optimization, which features include the following steps: Step 1. Perform weighted grayscale processing on the test image and the reference image, and perform histogram equalization; Step 2. Use the SIFT algorithm to extract feature points and filter them based on edge response values; Step 3. Use FLANN matcher to perform feature matching and combine Lowe's dynamic ratio test mechanism to screen matching pairs; Step 4. Using the improved PROSAC algorithm instead of the traditional RANSAC to estimate the homography matrix, the improved PROSAC algorithm includes matching point pre-sorting and adaptive iteration number control; Step 5. Perform image transformation based on the homography matrix and use multi-frequency image fusion and histogram normalization processing strategy to process the boundary area.

2. The high-precision image registration method based on SIFT feature extraction and PROSAC optimization according to claim 1, characterized in that: In step 3, the feature matching screening adopts an improved Lowe's dynamic ratio test mechanism, which calculates the distance ratio between the best matching point and the second best matching point and sets a dynamic threshold. When the ratio is less than the dynamic threshold, it is determined to be a valid matching pair, thereby improving the robustness and accuracy of feature matching.

3. The high-precision image registration method based on SIFT feature extraction and PROSAC optimization according to claim 1, characterized in that: In step 4, an improved PROSAC algorithm is used instead of the traditional RANSAC to estimate the homography matrix. The improved PROSAC algorithm includes pre-sorting of matching point quality, adaptive iteration number control, and the introduction of an improved Huber loss function to improve the robustness to outliers and accelerate computational convergence.

4. The high-precision image registration method based on SIFT feature extraction and PROSAC optimization according to claim 1, characterized in that: After completing the image transformation based on the homography matrix in step 5, the fusion processing includes adopting a multi-band image fusion strategy, in which low-frequency information is directly superimposed through the weighted Laplacian pyramid, high-frequency information is selected to retain key details through maximum gradient selection, and the image illumination is unified in combination with the adaptive histogram normalization method to achieve smooth transition in the boundary area and enhance the overall brightness consistency.

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