论文标题
纹理依赖性频率选择性重建非规范采样图像
Texture-Dependent Frequency Selective Reconstruction of Non-Regularly Sampled Images
论文作者
论文摘要
在许多情况下，只有在像素位置的非规范子集上可用的像素信息。但是，要进一步处理，需要在常规网格上重建此类图像。除许多其他算法外，还可以针对此任务应用频率选择性重建。它执行稀疏信号模型的块生成，作为傅立叶基函数的迭代叠加，并使用此模型替换图像中缺失或损坏的像素。在本文中，证明不需要在均质和异质区域上花费相同数量的迭代。因此，引入了一种频率选择性重建的新纹理依赖性方法，该方法根据要重建的区域的纹理分布迭代次数。与原始频率选择性重建相比，根据迭代次数的数量，可以实现高达1.47 dB的视觉上明显增长。
There exist many scenarios where pixel information is available only on a non-regular subset of pixel positions. For further processing, however, it is required to reconstruct such images on a regular grid. Besides many other algorithms, frequency selective reconstruction can be applied for this task. It performs a block-wise generation of a sparse signal model as an iterative superposition of Fourier basis functions and uses this model to replace missing or corrupted pixels in an image. In this paper, it is shown that it is not required to spend the same amount of iterations on both homogeneous and heterogeneous regions. Hence, a new texture-dependent approach for frequency selective reconstruction is introduced that distributes the number of iterations depending on the texture of the regions to be reconstructed. Compared to the original frequency selective reconstruction and depending on the number of iterations, visually noticeable gains in PSNR of up to 1.47 dB can be achieved.
