Oral: Denoising of Transmission Electron Microscopic Data

In-situ TEM is a powerful technique to understand dynamic changes in nanoparticles as they undergo phase transformation and structural deformation. As these transformations can occur spontaneously, conducting high-speed in-situ TEM studies to achieve atomic resolution information across a wide field of view while minimizing the impact of electron dose presents a significant challenge. The in-situ TEM video often contains high levels of noise, making it difficult to obtain structural and atomic information.



We have applied denoising methods to a TEM dataset affected by a combination of Poisson and Gaussian noise. A distinctive challenge in this study lies in the absence of ground truth or previously denoised images for benchmarking purposes. We use a variety of denoising techniques including Total Variation, Non-Local Means, Noise2Void, Noise2Fast, and BM3D with Anscombe transform. The denoising approaches reveal new features hidden in the noisy data. The denoised TEM images show a stronger contrast, making it easier for us to distinguish the boundary between the nanoparticle and the background (carbon support). Comparing the FFTs of the noisy and denoised images, we reveal an additional spot corresponding to the Fe₃O₄ (400) reflection. The FFT of the denoised image shows an increase in the clarity and number of “Thon” rings which suggests an increase in the information content of the image as a function of spatial frequency, or resolution.