Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, whe...Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter （PEF）, is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data＇s space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance.展开更多
In the technique of robot-assisted invasive surgery, high quality image is a key factor of the visual navigation system. In this paper, the authors have made a study of the image processing in visual system. Based on ...In the technique of robot-assisted invasive surgery, high quality image is a key factor of the visual navigation system. In this paper, the authors have made a study of the image processing in visual system. Based on the analysis of plentiful demising methods, they proposed a new method （S-AM-W） which oxnbines Adaptive Median filter and Wioaer filter to renmve the main noises （Salt ＆ Pepper noise and Gattssian noise）. The sinlflation results show that it is simple, well real time, and has high Peak Signal-to-Noise Ratio （PSNR）. It was found that the new method is effective and efficient in dealing with medical image of background noise.展开更多
基金supported by the National Natural Science Foundation of China(No.41474109)the China National Petroleum Corporation under grant number 2016A-33
文摘Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter （PEF）, is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data＇s space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance.
基金supported by the Henan Province Innovation and Technology Fund for Outstanding Scholarship(0421000500)the Key Scientific Research Projects of Henan University of Technology(09XZD008)the support of the Chinese National Programs for Hi-Tech R&D(2007AA704339)
文摘In the technique of robot-assisted invasive surgery, high quality image is a key factor of the visual navigation system. In this paper, the authors have made a study of the image processing in visual system. Based on the analysis of plentiful demising methods, they proposed a new method （S-AM-W） which oxnbines Adaptive Median filter and Wioaer filter to renmve the main noises （Salt ＆ Pepper noise and Gattssian noise）. The sinlflation results show that it is simple, well real time, and has high Peak Signal-to-Noise Ratio （PSNR）. It was found that the new method is effective and efficient in dealing with medical image of background noise.
