Super-Resolution Reconstruction Of Transmission Electron Microscopy Images Using Deep Learning

Deep learning techniques have shown promising outcomes in single image super-resolution (SR) reconstruction from noisy and blurry low resolution data. The SR reconstruction can cater the fundamental. limitations of transmission electron microscopy (TEM) imaging to potentially attain a balance among the trade-offs like imaging-speed, spatial/temporal resolution, and dose/exposure-time, which is often difficult to achieve simultaneously otherwise. In this work, we present a convolutional neural network (CNN) model, utilizing both local and global skip connections, aiming for 4 x SR reconstruction of TEM images. We used exact image pairs of a calibration grid to generate our training and independent testing datasets. The results are compared and discussed using models trained on synthetic (downsampled) and real data from the calibration grid. We also compare the variants of the proposed network with well-known classical interpolations techniques. Finally, we investigate the domain adaptation capacity of the CNN-based model by testing it on TEM images of a cilia sample, having different image characteristics as compared to the calibration-grid.