Image Stitching Algorithm Based on Phase Correlation and Levenberg-Marquardt (L-M)

For those interested in the image stitching algorithm based on phase correlation and Levenberg-Marquardt (L-M) optimization, detailed technical information is available. This algorithm effectively merges multiple images to create larger, more comprehensive composite images. The implementation typically involves two key phases: first, phase correlation calculates cross-power spectrum to detect translation offsets between images with sub-pixel accuracy, often implemented using FFT (Fast Fourier Transform) operations. Second, the L-M algorithm (a hybrid gradient descent and Gauss-Newton method) optimizes geometric transformations by minimizing intensity differences in overlapping regions, typically using Jacobian matrix calculations for nonlinear least-squares optimization. This approach is particularly valuable in computer vision and image processing, with applications spanning panoramic stitching (where it handles projective transformations), medical image registration (aligning multi-modal scans), and virtual reality environments (creating immersive backgrounds). The algorithm can be implemented using OpenCV's phaseCorrelate() function for initial alignment and custom L-M optimization loops for refinement. For specific implementation details, parameter tuning guidelines, or technical discussions, feel free to reach out for further assistance.