AI Article Synopsis
- The study addresses issues of image stitching in atomic force microscopy (AFM) due to dynamic noise causing disruptions like dislocation and tearing.
- It introduces a homography matrix optimization technique, which uses singular value decomposition to denoise the matrix and improve image alignment.
- Experimental results show that the new method outperforms other algorithms (SIFT + RANSAC, SIFT + AT, ORB) in achieving precise transformations and maintaining high-quality detail in wide-field image mosaics.
Article Abstract
During the offline combination of multiple atomic force microscopy (AFM) images, changes in probe displacement can be affected by dynamic noise, leading to dislocation and tearing in the stitching. To overcome this, we designed a homography matrix optimization method to describe the relative positional relationship between images by constructing the original image matrix and applying singular value decomposition to denoise the homography matrix. Additionally, we implemented a scale-invariant feature transform (SIFT) to extract feature points. To verify the effectiveness of this method, the SIFT + RANSAC (R), SIFT + affine transformation (AT), and oriented FAST and rotated BRIEF (ORB) algorithms were compared with the proposed algorithm. The experimental results demonstrate that the proposed method precisely computes the transformation matrix, thereby guaranteeing the geometric consistency of mosaic imaging. The proposed method preserves the intricate details of the original image and enhances the stitching quality of wide-field images.
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| http://dx.doi.org/10.1016/j.micron.2024.103730 | DOI Listing |
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