  The cryo-electron microscope (cryo-EM) is increasingly popular these years.
It helps to uncover the biological structures and functions of macromolecules.
In this paper, we address image denoising problem in cryo-EM. Denoising the
cryo-EM images can help to distinguish different molecular conformations and
improve three dimensional reconstruction resolution. We introduce the use of
data-driven tight frame (DDTF) algorithm for cryo-EM image denoising. The DDTF
algorithm is closely related to the dictionary learning. The advantage of DDTF
algorithm is that it is computationally efficient, and can well identify the
texture and shape of images without using large data samples. Experimental
results on cryo-EM image denoising and conformational classification
demonstrate the power of DDTF algorithm for cryo-EM image denoising and
classification.
