Super-resolution using DCT based learning with LBP as feature model

机译：使用基于LPT作为特征模型的基于DCT的学习实现超分辨率

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In this paper, we propose a novel learning based technique for feature preserving super-resolution of a low resolution observation. The local geometry of an image is conveyed by image features such as edges, corners and curves. We encode these features with local binary pattern operator. The missing high resolution features of the low resolution observation are learnt in the form of discrete cosine transform coefficients from high resolution images in the training database. Experiments are conducted on real world natural images and results are compared with the standard interpolation techniques. Both the qualitative and quantitative comparisons show the effectiveness of the proposed approach.

机译：在本文中，我们提出了一种新的基于学习的技术，用于保持低分辨率观测的超分辨率。图像的局部几何形状是通过图像特征（如边缘，拐角和曲线）传达的。我们使用本地二进制模式运算符对这些功能进行编码。从训练数据库中的高分辨率图像中以离散余弦变换系数的形式学习低分辨率观测的缺失高分辨率特征。对真实世界的自然图像进行了实验，并将结果与标准插值技术进行了比较。定性和定量比较都表明了该方法的有效性。

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来源
《2012 Third International Conference on Computing Communication amp; Networking Technologies.》|2012年|p.1- 6|共6页
会议地点 Coimbatore(IN);Coimbatore(IN)
作者
Pithadia Parul V.; Gajjar Prakash P.; Dave J. V.;
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作者单位

EC Department, L. D. College of Engineering, Ahmedabad, India;

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原文格式 PDF
正文语种 eng
中图分类通信网;通信网;
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Super-resolution using DCT based learning with LBP as feature model

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