Structure Preserving Image Interpolation via Adaptive 2D Autoregressive Modeling
Structure Preserving Image Interpolation via Adaptive 2D Autoregressive Modeling
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DOI：
10.1109/icip.2007.4379987
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发表时间：
2007-11
期刊：
影响因子：
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通讯作者：
Xiangjun Zhang;Xiaolin Wu
中科院分区：
文献类型：
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作者：
Xiangjun Zhang;Xiaolin Wu
图像插值的性能取决于在估计丢失像素时能够适应自然图像的非平稳统计的图像模型。然而，这样的自适应模型的构建需要的知识的每一个像素是缺席。我们通过一种新的分段2D自回归技术来解决这个难题，该技术建立模型并联合估计丢失的像素。该任务被表述为非线性优化问题。虽然计算量很大，但新的非线性方法在PSNR和主观视觉质量方面都比现有方法产生了上级结果。此外，在寻求一个实际的解决方案，我们打破了非线性优化问题的两个子问题的线性最小二乘估计。这种线性方法在我们的实验中证明是非常有效的。
The performance of image interpolation depends on an image model that can adapt to nonstationary statistics of natural images when estimating the missing pixels. However, the construction of such an adaptive model needs the knowledge of every pixels that are absent. We resolve this dilemma by a new piecewise 2D autoregressive technique that builds the model and estimates the missing pixels jointly. This task is formulated as a non-linear optimization problem. Although computationally demanding, the new non-linear approach produces superior results than current methods in both PSNR and subjective visual quality. Moreover, in quest for a practical solution, we break the non-linear optimization problem into two subproblems of linear least-squares estimation. This linear approach proves very effective in our experiments.