@Article{jihpp.2020.010657,
AUTHOR = {Jianquan Ouyang, Yi He, Huanrong Tang, Zhousong Fu},
TITLE = {Research on Denoising of Cryo-em Images Based on Deep Learning},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {2},
YEAR = {2020},
NUMBER = {1},
PAGES = {1--9},
URL = {http://www.techscience.com/jihpp/v2n1/40331},
ISSN = {2637-4226},
ABSTRACT = {Cryo-em (Cryogenic electron microscopy) is a technology this can 
build bio-macromolecule of three-dimensional structure. Under the condition of 
now, the projection image of the biological macromolecule which is collected by 
the Cryo-em technology that the contrast is low, the signal to noise is low, image 
blurring, and not easy to distinguish single particle from background, the 
corresponding processing technology is lagging behind. Therefore, make Cryoem image denoising useful, and maintaining bio-macromolecule of contour or 
signal of function-construct improve Cryo-em image quality or resolution of 
Cryo-em three-dimensional structure have important effect. This paper 
researched a denoising function base on GANs (generative adversarial networks), 
purpose an improved discriminant model base on Wasserstein distance and an 
improved image denoising model by add gray constraint. Our model turn 
discriminant model’s training process from binary classification’s training 
process into regression task training process, it make GANs in training process 
more stable, more reasonable parameter passing. Meantime, we also propose an 
improved generative model by add gray constraint. The experimental results 
show that our model can increase the peak signal-to-noise ratio of the Cryo-em 
simulation image by 10.3 dB and improve SSIM (Structural Similarity Index) of 
the denoised image results by 0.43. Compared with traditional image denoising 
algorithms such as BM3D (Block Matching 3D), our model can better save the 
model structure and the vein signal in the original image and the operation speed 
is faster.},
DOI = {10.32604/jihpp.2020.010657}
}