Obtaining high-quality images from physical systems, objects, and processes is fundamental for a myriad of areas of science and technology. However, in many situations, the measured images contain defects and/or are accompanied by noise, degrading the quality of the measurement. Recently, a variant of the well-known Talbot self-imaging effect has been shown to redistribute the energy of a spatially periodic collection of images, obtaining output images with increased energy with respect to the input ones. In this work we experimentally demonstrate that such an energy redistribution method has the unique capabilities of increasing the coherent energy level of a periodic set of images over that of the incoherent noise, even allowing images completely buried under noise to be recovered. We further demonstrate that the process can mitigate potential faults of the periodic image structure, including blocked images, spatial jitter, and coherent noise, offering important enhancements (e.g., in regards to the quality of the recovered individual images) in the self-healing capabilities of Talbot self-imaging.

中文翻译：

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定期故障图像的广义Talbot自修复和噪声缓解。

从物理系统，对象和过程中获取高质量图像对于无数的科学和技术领域都是至关重要的。然而，在许多情况下，测量的图像包含缺陷和/或伴随有噪声，从而降低了测量的质量。近来，已经示出了众所周知的塔尔博特（Talbot）自成像效果的变体来重新分配图像的空间周期性集合的能量，从而获得相对于输入图像具有增加的能量的输出图像。在这项工作中，我们通过实验证明了这种能量重新分配方法具有独特的功能，可以将周期性图像集的相干能级提高到非相干噪声的能级以上，甚至可以恢复完全被噪声掩埋的图像。