Anada Satoshi, Nomura Yuki, Hirayama Tsukasa, Yamamoto Kazuo
Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta-ku, Nagoya, Aichi 456-8587, Japan.
Technology Innovation Division, Panasonic Corporation, 3-1-1 Yagumo-Nakamachi, Moriguchi, Osaka 570-8501, Japan; Department of Crystalline Materials Science, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8603, Japan.
Ultramicroscopy. 2019 Nov;206:112818. doi: 10.1016/j.ultramic.2019.112818. Epub 2019 Jul 26.
We demonstrate the effect of image denoising with sparse coding and dictionary learning algorithms for low-dose electron holography. Electron interference patterns (holograms) of a GaAs semiconductor specimen having a p-n junction were recorded with different exposure times (1, 4 and 40 s) and computer algorithms were applied to the holograms. The algorithms reduced the noise in the low-dose holograms successfully, with high data fidelity. In addition, the denoised holograms resulted in the phase images of a higher signal-to-noise ratio, fitting well to those obtained from original holograms recorded with sufficiently-long exposure times. The standard deviation in the reconstructed phase images was reduced by one digit using the denoising process. These results indicate that the sparse coding with dictionary learning algorithms are effective for electron holography and can potentially improve the temporal resolution by a factor of 40 or more without deterioration in the spatial resolution, thus enabling the observation of materials sensitive to electron beam irradiation and high-speed dynamical in situ electron holography.
我们展示了使用稀疏编码和字典学习算法对低剂量电子全息图进行图像去噪的效果。用不同的曝光时间（1、4和40秒）记录了具有p-n结的GaAs半导体样品的电子干涉图案（全息图），并将计算机算法应用于这些全息图。这些算法成功地降低了低剂量全息图中的噪声，具有高数据保真度。此外，去噪后的全息图产生了更高信噪比的相位图像，与用足够长曝光时间记录的原始全息图所获得的相位图像拟合良好。使用去噪过程，重建相位图像中的标准偏差降低了一位数。这些结果表明，带有字典学习算法的稀疏编码对电子全息术是有效的，并且有可能在不降低空间分辨率的情况下将时间分辨率提高40倍或更多，从而能够观察对电子束辐照敏感的材料以及进行高速动态原位电子全息术。
