A High-Efficiency Super-Resolution Reconstruction Algorithm from Image/Video Sequences

The topic of super-resolution image reconstruction has recently received considerable attention within the research area. Super-resolution image reconstruction methods attempt to create a single high-resolution image from a number of low-resolution images (or a video sequence). The Bayesian maximum a posteriori (MAP) estimation method is widely used for super-resolution image reconstruction. So far, existing super-resolution reconstruction methods are all confronted with the problem of slow convergence and expensive computation. To satisfy the requirement of real-time application, we propose a high-efficiency super-resolution reconstruction algorithm that solves two key bottlenecks in the multi-frame MAP framework. The first breakthrough is to select the Armijo rule to identify the step length instead of the exact line search. The second one is to approximately compute the gradient of the MAP objective function using analytic representation instead of numerical calculation. Experimental results show that the proposed algorithm is applicable to real-time super-resolution reconstruction of low-resolution image sequences.