A Noise Extraction Method for Cryo-EM Single-Particle Denoising

Cryo-Electron Microscopy(cryo-EM)has become a powerful method to study the structure and function of biological macromolecules.However,in clustering tasks based on the projection angle of particles in cryoEM,the noise considerably affects the clustering results.Existing denoising algorithms are ineffective due to the extremely low signal-to-noise ratio(SNR)of cryo-EM images and the complexity of noise types.The noise of a single particle greatly influences the orientation estimation of the subsequent clustering task,and the result of the clustering task directly affects the accuracy of the 3D reconstruction.In this paper,we propose a construction method of cryo-EM denoising dataset that uses U-Net to extract noise blocks from cryoEM images,superimpose the noise block with the projected pure particles to construct our simulated dataset.Then we adopt a supervised generative adversarial network(GAN)with perceptual loss to train on our simulated dataset and denoise the real cryo-EM single particle.The method can solve the problem of poor denoising performance caused by assuming that the noise of the Gaussian distribution does not conform to the noise distribution of cryo-EM,and it can retain the useful information of particles to a great extent.We compared traditional image filtering methods and the classic deep learning denoising algorithm DnCNN on the simulated and real datasets.Experiment results show that the method based on deep learning has more advantages than traditional image denoising methods.It is worth mentioning that our method achieves a competitive peak signal to noise ratio(PSNR)and structural similarity(SSIM).Moreover,visualization results,indicate that our method can retain the structure information and orientation information of particles to a greater extent compared with other state-of-the-art image denoising methods.It means that our denoising task can provide considerable help for subsequent cryo-EM clustering tasks.

Signal extraction for GPS deformation monitoring in mining survey

The basic signal model of deformation monitoring with GPS was introduced and the main problems of GPS deformation monitoring in mining area were discussed. For the problem of noise signal extraction in GPS deformation monitoring, the Kalman-EMD method was proposed to obtain the effective deformation signal. The reliability and effectiveness of the methodology were tested and verified by analog signal. The results of experiment in Mongolia show that the accuracy of the proposed GPS deformation monitoring model is equivalent to that of level method.

An improved method to distinguish between scanned and rasterized document images

Office automation （OA） has evolved with the development of computer science,improving staff efficiency.Unstructured information processing is an important aspect of OA; therefore,in this paper,we propose an efficient method for distinguishing scanned and rasterized document images which can be used in this process.To ensure the efficiency and precision of our method,two steps are included：rapid processing and classification using noise features.In the first step,color,skew,and isolated noise features are used to identify the source of the images.In the second step,noise features are extracted from the input image and a support vector machine （SVM） classifier is used for classification.Our experiments show that our method has high precision and speed for distinguishing scanned and rasterized document images.

Feature extraction of ship-radiated noise using higher-order spectrum

The features of the ship noises are analyzed by using the higher-order spectrum (HOS) after studying their distribution. The results show that the different ship noise has different ranges of the main frequency. The main frequencies of the first class ships are less than 120 Hz, while the second class ships drop in 130 Hz -- 320 Hz. The different relationship between w1 and w2 corresponds to different bispectrum graph. There are the same results in the trispectrum. The feature vector is consist of the wls which correspond to the maximum bispectrum B(wl, wl) and the maximum trispectrum B(wl, w1,wl) respectively, the al, w2 which correspond to the maximum bispectrum B(wl, w2).