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Novel Graph Cuts Method for Multi-Frame Super-Resolution
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DOI：10.1109/LSP.2015.2477079.png)
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摘要
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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.
Keyword:
alpha-expansion
energy approximation
graph cuts
super-resolution
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9.6
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1.1W
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1.7W
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