Resampling Images to a Regular Grid From a Non-Regular Subset of Pixel Positions Using Frequency Selective Reconstruction
Resampling Images to a Regular Grid From a Non-Regular Subset of Pixel Positions Using Frequency Selective Reconstruction
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DOI：
10.1109/tip.2015.2463084
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发表时间：
2015-07
影响因子：
10.6
通讯作者：
Jürgen Seiler;Markus Jonscher;M. Schöberl;André Kaup
中科院分区：
文献类型：
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作者：
Jürgen Seiler;Markus Jonscher;M. Schöberl;André Kaup
即使图像信号通常被定义在规则的2D网格上，也存在许多情况，其中情况并非如此，并且图像信号的幅度仅可用于像素位置的非规则子集。在这种情况下，必须执行将图像重新定位到规则网格。这是必要的，因为几乎所有用于处理、传输或显示图像信号的算法和技术都依赖于在规则网格上可用的样本。因此，在该规则网格上重建图像是非常重要的，使得重建最接近于信号最初在规则网格上采集的情况。在本文中，频率选择性重建的介绍，以解决这一具有挑战性的任务。该算法利用傅立叶域中图像的小区域可以稀疏表示的特性来重构图像信号。通过进一步考虑成像系统的光学传递函数的基本性质，迭代地生成信号的稀疏模型。在这样做时，所提出的算法是能够实现一个非常高的重建质量，在峰值信噪比（PSNR）和结构相似性度量以及在视觉质量方面。仿真结果表明，该算法是能够优于国家的最先进的重建算法和增益超过1 dB的PSNR是可能的。
Even though image signals are typically defined on a regular 2D grid, there also exist many scenarios where this is not the case and the amplitude of the image signal only is available for a non-regular subset of pixel positions. In such a case, a resampling of the image to a regular grid has to be carried out. This is necessary since almost all algorithms and technologies for processing, transmitting or displaying image signals rely on the samples being available on a regular grid. Thus, it is of great importance to reconstruct the image on this regular grid, so that the reconstruction comes closest to the case that the signal has been originally acquired on the regular grid. In this paper, Frequency Selective Reconstruction is introduced for solving this challenging task. This algorithm reconstructs image signals by exploiting the property that small areas of images can be represented sparsely in the Fourier domain. By further considering the basic properties of the optical transfer function of imaging systems, a sparse model of the signal is iteratively generated. In doing so, the proposed algorithm is able to achieve a very high reconstruction quality, in terms of peak signal-to-noise ratio (PSNR) and structural similarity measure as well as in terms of visual quality. The simulation results show that the proposed algorithm is able to outperform state-of-the-art reconstruction algorithms and gains of more than 1 dB PSNR are possible.