CNR-National Institute of Optics, Pozzuoli (NA), Italy.
Opt Lett. 2013 Mar 1;38(5):619-21. doi: 10.1364/OL.38.000619.
Holographic imaging may become severely degraded by a mixture of speckle and incoherent additive noise. Bayesian approaches reduce the incoherent noise, but prior information is needed on the noise statistics. With no prior knowledge, one-shot reduction of noise is a highly desirable goal, as the recording process is simplified and made faster. Indeed, neither multiple acquisitions nor a complex setup are needed. So far, this result has been achieved at the cost of a deterministic resolution loss. Here we propose a fast non-Bayesian denoising method that avoids this trade-off by means of a numerical synthesis of a moving diffuser. In this way, only one single hologram is required as multiple uncorrelated reconstructions are provided by random complementary resampling masks. Experiments show a significant incoherent noise reduction, close to the theoretical improvement bound, resulting in image-contrast improvement. At the same time, we preserve the resolution of the unprocessed image.
全息成像可能会因散斑和非相干附加噪声的混合而严重退化。贝叶斯方法可以降低非相干噪声，但需要有关噪声统计数据的先验信息。在没有先验知识的情况下，单次降噪是一个非常理想的目标，因为记录过程得到简化并且变得更快。实际上，既不需要多次采集，也不需要复杂的设置。到目前为止，为了实现确定性分辨率损失，已经取得了这一结果。在这里，我们提出了一种快速的非贝叶斯去噪方法，通过对运动扩散器进行数值合成来避免这种权衡。通过这种方式，仅需要一个单一的全息图，因为通过随机互补重采样掩模提供了多个不相关的重建。实验表明，噪声得到了显著降低，接近理论改进边界，从而提高了图像对比度。同时，我们保留了未处理图像的分辨率。
