Research on Denoising of Cryo-em Images Based on Deep Learning

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 Cryo-em 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.

Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis

While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising,existing methods mostly rely on simple noise assumptions,such as additive white Gaussian noise(AWGN),JPEG compression noise and camera sensor noise,and a general-purpose blind denoising method for real images remains unsolved.In this paper,we attempt to solve this problem from the perspective of network architecture design and training data synthesis.Specifically,for the network architecture design,we propose a swin-conv block to incorporate the local modeling ability of residual convolutional layer and non-local modeling ability of swin transformer block,and then plug it as the main building block into the widely-used image-to-image translation UNet architecture.For the training data synthesis,we design a practical noise degradation model which takes into consideration different kinds of noise(including Gaussian,Poisson,speckle,JPEG compression,and processed camera sensor noises)and resizing,and also involves a random shuffle strategy and a double degradation strategy.Extensive experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance and the new degradation model can help to significantly improve the practicability.We believe our work can provide useful insights into current denoising research.The source code is available at https://github.com/cszn/SCUNet.

A statistical learning based image denoising approach

The image denoising is a very basic but important issue in the field of image procession. Most of the existing methods addressing this issue only show desirable performance when the image complies with their underlying assumptions. Especially, when there is more than one kind of noises, most of the existing methods may fail to dispose the corresponding image. To address this problem, we propose a two-step image denoising method motivated by the statistical learning theory. Under the proposed framework, the type and variance of noise are estimated with support vector machine （SVM） first, and then this information is employed in the proposed denoising algorithm to further improve its denoising performance. Finally, comparative study is constructed to demonstrate the advantages and effectiveness of the proposed method.