Research on Improved Multi-Channel Image Stitching Technology Based on Fast Algorithms

Abstract

The image registration and fusion process of image stitching algorithms entails significant computational costs, and the use of robust stitching algorithms with good performance is limited in real-time applications on PCs (personal computers) and embedded systems. Fast image registration and fusion algorithms suffer from problems such as ghosting and dashed lines, resulting in suboptimal display effects on the stitching. Consequently, this study proposes a multi-channel image stitching approach based on fast image registration and fusion algorithms, which enhances the stitching effect on the basis of fast algorithms, thereby augmenting its potential for deployment in real-time applications. First, in the image registration stage, the gridded Binary Robust Invariant Scalable Keypoints (BRISK) method was used to improve the matching efficiency of feature points, and the Grid-based Motion Statistics (GMS) algorithm with a bidirectional rough matching method was used to improve the matching accuracy of feature points. Then, the optimal seam algorithm was used in the image fusion stage to obtain the seam line and construct the fusion area. The seam and transition areas were fused using the fade-in and fade-out weighting algorithm to obtain smooth and high-quality stitched images. The experimental results demonstrate the performance of our proposed method through an improvement in image registration and fusion metrics. We compared our approach with both the original algorithm and other existing methods and achieved significant improvements in eliminating stitching artifacts such as ghosting and discontinuities while maintaining the efficiency of fast algorithms.

Quantitative phase image stitching guided by reconstructed intensity images in one-shot double field of view multiplexed digital holographic microscopy

“…In the double FOV stitching module, The FOV2 image is linearly transformed through the affine transformation matrix, and then registered with the FOV1 image to achieve rough stitching of the intensity or phase images of the two FOVs. Finally, weighted fusion algorithm [ 35 ] is used to optimize the rough stitching result and gradually reduce the difference between the original image and the registered image. Figures 2(f) and 2(g) are the intensity image and 3D phase distribution with extended FOV after weighted fusion, respectively.…”

Rectification for Stitched Images with Deformable Meshes and Residual Networks

“…With the rapid development of image stitching and image fusion technologies, methods for obtaining multi-view or even global perspectives through multiple single viewpoints have been widely applied in human production and daily life [1][2][3][4][5]. For instance, the extensive use of technologies such as panoramic images, autonomous driving, and virtual reality (VR) enables the precise remote observation of scenes by individuals [6][7][8].…”

Rectification for Image Stitching with Deformable Meshes and Residual Networks

“…With the rapid development of image stitching and image fusion technologies, methods for obtaining multi-view or even global perspectives through multiple single viewpoints have been widely applied in human production and daily life [1][2][3][4][5]. For instance, the extensive use of technologies such as panoramic images, autonomous driving, and virtual reality (VR) enables precise remote observation of scenes by individuals [6][7][8][9].…”