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| Super-Resolution through Dictionary Learning and Sparse Representation |
| PU Jian,ZHANG Jun-Ping |
| Shanghai Key Laboratory of Intelligent Information Processing,School of Computer Science and Engineering,Fudan University,Shanghai 200433 |
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Abstract The overcomplete dictionary extracted from large scale dataset and sparse representation have been widely applied in image denoise, deblocking and inpainting in recent years. However, this technique can not be directly employed to deal with heterogeneous low resolution and high resolution image patches and relevant image reconstruction with super-resolution as well. The method to yield the sparse representation meeting two overcomplete dictionaries of different scales at the same time is proposed in this paper and the super-resolution reconstruction of image sparse representation is implemented by it. To further improve the super-resolution effect of color images, the UV chroma super-resolution reconstruction based on super-resolution luminance information is put forward as well. The experimental results show the method in this paper obtain better outcome no matter in visual effects or in root mean squared (RMS) error.
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Received: 27 April 2009
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2010, Vol. 23 
