Novel Graph Cuts Method for Multi-Frame Super-Resolution

Abstract

In this letter, we propose a new graph cuts multi-frame super resolution method. The method is carried out in 3 steps. First, we project each high-resolution pixel $p$ onto the low-resolution images and select low-resolution pixels which fall within the zone of influence of $p$ . Second, we weigh the contribution of the low-resolution pixels via a soft switching function and add them to construct a virtual low resolution pixel. The high resolution image is then recovered after minimizing a Maximum a posteriori Markov Random Field (MAP-MRF) energy function. This is done by approximating our energy function to make it graph representable and minimize it with a graph cuts $\alpha $ -expansion algorithm. Experimental results show that our approach outperforms state-of-the-art methods.