Figure 7
From: Multi-resolution convolutional neural networks for inverse problems
Validation of the denoising application on heavily-noised aberration-corrected high-annular dark-field (HAADF) STEM images of sub-nanometre sized Platinum clusters. MCNN performs well on heavily-noised datasets, as is demonstrated in (a). The numbers on the upper-left corner are the SNRs and the upper-right are MAEs. Also MCNN gives out clear and consistent results on consecutive experimental image frames shown in the left columns, which are recorded at 150 fps with 128 × 128 pixels (b) and at 15 fps with 512 × 512 pixels (c), and are taken under electron dose in range [105, 106]eÅ−2s−1 with a FEI Titan Themis. The upper images are for the first frames, the lower images are for the second frames. Similar denoising results produced by PGURE-SVT are shown in the middle columns for a comparison, which are not as clear as the MCNN results in the right columns. The neural network without the LPFs layer can still predict clear result, if the input images have the same noisy features, as is shown in the upper rows of (d); but when applying this model to experimental images, the neural network equipped with the LPFs layer gives much better result than conventional neural network such as the recent noise2void model and DnCNN model. Fine-running the model with the LPFs layer by connecting a conditional generative adversarial network (GAN), the clusters in the predicted image are more atomic-like, as is shown in lower rows of (d).
