Reducing randomness of non-regular sampling masks for image reconstruction
Reducing randomness of non-regular sampling masks for image reconstruction
复制标题
减少图像重建的非规则采样掩模的随机性
DOI：
--
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
André Kaup
中科院分区：
文献类型：
--
作者：
Markus Jonscher;Jürgen Seiler;T. Richter;André Kaup
提高空间图像分辨率是图像采集中经常需要的，但具有挑战性的任务。最近，已经表明，可以通过用非规则采样掩模覆盖低分辨率传感器来获得高分辨率图像。然而，由于掩蔽，所得到的高分辨率图像中的一些像素信息是不可用的，并且必须通过有效的图像重建算法来重建，以便得到完全重建的高分辨率图像。在本文中，不同的采样掩模的影响与减少的随机性的非规则性的图像重建过程中进行评估。仿真结果表明，它是足够的，只在一个较小的尺度上使用非规则的采样掩码。与在整个图像传感器尺寸上不规则的任意选择的采样掩模相比，这些采样掩模导致PSNR的视觉上明显的增益。同时，它们简化了制造过程，并允许高效存储。
Increasing spatial image resolution is an often required, yet challenging task in image acquisition. Recently, it has been shown that it is possible to obtain a high resolution image by covering a low resolution sensor with a non-regular sampling mask. Due to the masking, however, some pixel information in the resulting high resolution image is not available and has to be reconstructed by an efficient image reconstruction algorithm in order to get a fully reconstructed high resolution image. In this paper, the influence of different sampling masks with a reduced randomness of the non-regularity on the image reconstruction process is evaluated. Simulation results show that it is sufficient to use sampling masks that are non-regular only on a smaller scale. These sampling masks lead to a visually noticeable gain in PSNR compared to arbitrary chosen sampling masks which are non-regular over the whole image sensor size. At the same time, they simplify the manufacturing process and allow for efficient storage.