Single Image Super-Resolution Reconstruction Technique based on A Single Hybrid Dictionary
Single Image Super-Resolution Reconstruction Technique based on A Single Hybrid Dictionary
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DOI：
10.1007/s11042-016-4022-x
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发表时间：
2016-11
影响因子：
3.6
通讯作者：
Chanzi Liu;Qingchun Chen;Hengchao Li
中科院分区：
文献类型：
--
作者：
Chanzi Liu;Qingchun Chen;Hengchao Li
本文提出了一种新的稀疏域方法，以实现基于单个混合字典的单图像超分辨率（SR）重建，该混合字典是从高分辨率（HR）图像块样本和低分辨率（LR）图像块样本的混合推导出来的。此外，还提出了一种线性模型来表征同一混合字典上 HR 图像块的稀疏表示与相应的 LR 图像块的稀疏表示之间的关系。结果表明，可以放宽对 HR 和 LR 图像块在相应的 HR 字典和 LR 字典上具有相同稀疏表示的要求。结果表明，使用单个混合字典不仅可以提供更灵活的框架来保持HR补丁和相应的退化LR补丁之间相似的稀疏特性，而且还可以适应它们的差异。在此基础上，重新表述了基于稀疏域的SR重建问题。此外，所提出的同一混合字典上 HR 补丁和相应 LR 补丁的稀疏表示之间的线性模型为我们提供了一种解释稀疏域中图像退化特征的新方法。最后，给出了实际的实验结果来测试和验证所提出的 SR 方法。
A new sparse domain approach is proposed in this paper to realize the single image super-resolution (SR) reconstruction based upon one single hybrid dictionary, which is deduced from the mixture of both the high resolution (HR) image patch samples and the low resolution (LR) ones. Moreover, a linear model is proposed to characterize the relationship between the sparse representations of both the HR image patches and the corresponding LR ones over the same hybrid dictionary. It is shown that, the requirement on the identical sparse representation of both HR and LR image patches over the corresponding HR dictionary and the LR dictionary can be relaxed. It is unveiled that, the use of one single hybrid dictionary can not only provide a more flexible framework to keep the similar sparse characteristics between the HR patches and the corresponding degenerated LR patches, but also to accommodate their differences. On this basis, the sparse domain based SR reconstruction problem is reformulated. Moreover, the proposed linear model between the sparse representations of both the HR patch and the corresponding LR patch over the same hybrid dictionary offers us a new method to interpret the image degeneration characteristics in sparse domain. Finally, practical experimental results are presented to test and verify the proposed SR approach.