Robust multiframe super-resolution reconstruction based on regularization

Images of high-resolution are desired and often required in most photoelectronic imaging applications, and corresponding image restoration algorithm has became the frontier research. A novel multiframe super-resolution reconstruction algorithm based on stochastic regularization is proposed in this paper. By analyzing the image degradation model, the iterative gradient method based on Taylor series expansion is applied in the algorithm to estimate the inter-frame displacement. The L₁ norm is used for fusing the data of low-resolution frames and removing outliers, and the regularization technique based on bilateral total variation is used to remove artifacts from the final answer and improve the rate of convergence. Simulated and real experiment results confirm the effectiveness of the algorithm.