收稿日期: 2015-12-25
修回日期: 2016-03-15
网络出版日期: 2016-09-15
基金资助
Supported by the National Nature Science Foundation of China (11331012,11571014,71271204)
Super resolution reconstruction of single image with denoising and upscaling
Received date: 2015-12-25
Revised date: 2016-03-15
Online published: 2016-09-15
Supported by
Supported by the National Nature Science Foundation of China (11331012,11571014,71271204)
刘晓 , 郭田德 , 韩丛英 , 李明强 . 有去噪和尺度放大的单幅图像超分辨率重建[J]. 中国科学院大学学报, 2016 , 33(5) : 596 -603 . DOI: 10.7523/j.issn.2095-6134.2016.05.004
In this study, a novel approach to single image resolution reconstruction is proposed based on nonlocal means, total variation-regularization, and sparse coding. Firstly, low-resolution (LR) image is denoised by nonlocal means which preserves geometric structure well. Then, high-resolution (HR) regularization-based component is obtained from up-scaling the LR image by using a reconstruction model. Image generation process is combined with total-variation regularization so that new image maintains part of the sharpness of the edges and some details. Meanwhile, the HR learning-based component is reconstructed by exploring the sparse coding which explores the co-occurrence relationship between LR training patches and their corresponding HR high-frequency patches. The regularization-based component and the learning-based component are combined to obtain an initial HR image. Finally, the global reconstruction constraint is applied to the initial image for making the final HR image natural. Experimental results show that our method is natural and robust.
Key words： super resolution; nonlocal means; total variation; sparse coding
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