Poisson Image Blending Algorithm Implementation

I have developed my own implementation of the Poisson image blending algorithm. The algorithm operates by solving Poisson equations in the gradient domain, where the key implementation involves constructing sparse linear systems that represent the Laplacian operator and solving them using numerical methods like conjugate gradient descent. Potential improvements to enhance performance and results include incorporating advanced image processing techniques such as multi-resolution blending through Gaussian pyramids. This could involve implementing pyramid decomposition using cv2.pyrDown() and pyrUp() functions, then applying Poisson blending at each level before recombination. Additional optimization strategies could involve implementing more efficient linear solvers (such as preconditioned conjugate gradient methods) and optimizing memory management for large image operations. The algorithm's stability could be enhanced by adding boundary condition handling and regularization terms to the Poisson equation formulation. The implementation currently handles basic gradient preservation and seamless integration between source and target images. Future enhancements could include automatic mask generation, color space optimization, and parallel processing using GPU acceleration with frameworks like CUDA or OpenCL. These potential improvements are designed to benefit those studying image processing and Poisson image blending algorithms, providing a foundation for understanding both the mathematical principles and practical implementation challenges in gradient-domain image editing.