L1-Norm Based Multi-Frame Image Super-Resolution Reconstruction Algorithm

The L1-norm based multi-frame image super-resolution reconstruction algorithm is an advanced method designed to enhance both reconstruction precision and computational efficiency. This algorithm incorporates significant improvements over conventional approaches by employing L1-norm regularization as a constraint term in the optimization process. The L1-norm constraint effectively preserves image details and textures by promoting sparsity in the solution, making it particularly suitable for handling edge information and fine structures. Implementation typically involves solving an optimization problem through iterative methods such as gradient descent or alternating direction method of multipliers (ADMM), where the data fidelity term combines information from multiple input frames. By leveraging temporal information from multiple low-resolution images, the algorithm achieves superior reconstruction quality through robust fusion and registration techniques. This method demonstrates broad application potential in image processing domains, including image enhancement, restoration, and computational photography applications, with practical implementations often utilizing matrix operations and convolution-based warping functions for efficient computation.