Transmission Electron Microscopy (TEM) is commonly used for structural analysis at the nm scale in material and biological sciences. Fast acquisition and low dose are desired to minimize the influence of external factors on the acquisition as well as the interaction of electrons with the sample. However, the resulting images are very noisy, which affects both manual and automated analysis. We present a comparative study of block matching, wavelet domain, energy minimization, and deep convolutional neural network based approaches to de-noise short exposure high-resolution TEM images of cilia. In addition, we evaluate the effect of denoising before or after registering multiple short exposure images of the same imaging field to further enhance fine details.