A Seismic Image Denoising Method Based on Kernel-prediction CNN Architecture

2020 ◽  
Vol 29 (07n08) ◽  
pp. 2040012
Author(s):  
Li Lou ◽  
Yong Li
Keyword(s):  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Denoising Method ◽  
Spatial Feature ◽  
Residual Noise ◽  
Seismic Image ◽  
Feature Extractor ◽  
Seismic Images ◽  
Asymmetric Loss Function ◽  
Image Details

To filter noises and preserve the details of seismic images, a denoising method based on kernel prediction convolution neural network (CNN) architecture is proposed. The method consists of two convolution layers and a residual connection, containing a source sensing encoder, a spatial feature extractor and a kernel predictor. The scalar kernel was normalized by the softmax function to obtain the denoised images. In addition, to avoid excessive blur at the expense of image details, the authors put forward the concept of asymmetric loss function, which would enable users to control the level of residual noise and make a trade-off between variance and deviation. The experimental results show the proposed method achieved good denoising effect. Compared with some other excellent methods, the proposed method increased the peak signal-to-noise ratio (PSNR) by about 1.0–3.2 dB for seismic images without discontinuity, and about 1.8–3.9 dB for seismic images with discontinuity.

Research on LIFT Technology for 3D Seismic Denoising in Coalfield

2014 ◽  
Vol 556-562 ◽  
pp. 6328-6331
Author(s):  
Su Zhen Shi ◽  
Yi Chen Zhao ◽  
Li Biao Yang ◽  
Yao Tang ◽  
Juan Li
Keyword(s):  
Signal To Noise Ratio ◽  
High Signal ◽  
3D Seismic ◽  
Signal To Noise ◽  
Denoising Method ◽  
Seismic Processing ◽  
Work Area ◽  
Noise Data ◽  
Noise Ratio ◽  
Effective Signal

The LIFT technology has applied in process of denoising to ensure the imaging precision of minor faults and structure in 3D coalfield seismic processing. The paper focused on the denoising process in two study areas where the LIFT technology is used. The separation of signal and noise is done firstly. Then denoising would be done in the noise data. The Data of weak effective signal that is from the noise data could be blended with the original effective signal to reconstruct the denoising data, so the result which has high signal-to-noise ratio and preserved amplitude is acquired. Thus the fact shows that LIFT is an effective denoising method for 3D seismic in coalfield and could be used widely in other work area.

Preliminary study of improved median filter using adaptively mask size in light microscopic image

Microscopy ◽  
2020 ◽  
Vol 69 (1) ◽  
pp. 31-36
Author(s):  
Ji-Youn Kim ◽  
Youngjin Lee
Keyword(s):  
Image Quality ◽  
Coefficient Of Variation ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Microscopic Image ◽  
Signal To Noise ◽  
Mask Size ◽  
Preliminary Study ◽  
Noise Ratio ◽  
Image Details

Abstract This study aimed to develop and evaluate an improved median filter (IMF) with an adaptive mask size for light microscope (LM) images. We acquired images of the mouse first molar using a LM at 100× magnification. The images obtained using our proposed IMF were compared with those from a conventional median filter. Several parameters such as the contrast-to-noise ratio, coefficient of variation, no-reference assessments and peak signal-to-noise ratio were employed to evaluate the image quality quantitatively. The results demonstrated that the proposed IMF could effectively de-noise the LM images and preserve the image details, achieving a better performance than the conventional median filter.

INTEGRATED TECHNIQUES FOR DETERMINING THE VELOCITY FUNCTION IN THE SEISMIC REFLECTION: EXAMPLE OF LOW SIGNAL-TO-NOISE DATA

2016 ◽  
Vol 34 (2) ◽  
Author(s):  
Rodrigo Francis Revorêdo ◽  
Carlos César Nascimento da Silva
Keyword(s):  
Seismic Reflection ◽  
Signal To Noise Ratio ◽  
Hydrocarbon Exploration ◽  
Velocity Analysis ◽  
Signal To Noise ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Noise Data ◽  
Quality And Reliability ◽  
Seismic Images

ABSTRACT. In the hydrocarbon exploration activities, the reprocessing of old seismic reflection data, acquired with few channels and with low signal-to-noise ratio, is commonly undertaken to ameliorate the quality and reliability of the seismic images...Keywords: seismic processing, velocity analysis, CVS. RESUMO. É comum na exploração de hidrocarbonetos o reprocessamento de dados sísmicos antigos, por vezes com um baixo número de canais e baixa razão sinal/ruído, com o objetivo de gerar uma imagem de melhor qualidade e confiabilidade quando comparada àquelas já existentes...Palavras-chave: processamento sísmico, análise de velocidades, CVS. 

A Robust Hybrid Filter Based on Evolutionary Intelligence and Fuzzy Evaluation

2018 ◽  
Vol 18 (04) ◽  
pp. 1850023 ◽  
Author(s):  
Hadi Salehi ◽  
Javad Vahidi ◽  
Homayun Motameni
Keyword(s):  
Genetic Algorithm ◽  
Image Quality Assessment ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Bilateral Filter ◽  
Fuzzy Evaluation ◽  
Signal To Noise ◽  
Denoising Method ◽  
Final Model ◽  
Noise Ratio

In this paper, a novel denoising method based on wavelet, extended adaptive Wiener filter and the bilateral filter is proposed for digital images. Production of mode is accomplished by the genetic algorithm. The proposed extended adaptive Wiener filter has been developed from the adaptive Wiener filter. First, the genetic algorithm suggest some hybrid models. The attributes of images, including peak signal to noise ratio, signal to noise ratio and image quality assessment are studied. Then, in order to evaluate the model, the values of attributes are sent to the Fuzzy deduction system. Simulations and evaluations mentioned in this paper are accomplished on some standard images such as Lena, boy, fruit, mandrill, Barbara, butterfly, and boat. Next, weaker models are omitted by studying of the various models. Establishment of new generations performs in a form that a generation emendation is carried out, and final model has a more optimum quality compared to each two filters in order to obviate the noise. At the end, the results of this system are studied so that a comprehensive model with the best performance is to be found. Experiments show that the proposed method has better performance than wavelet, bilateral, Butterworth, and some other filters.

Research on Application of Wavelet Denoising Method Based on Signal to Noise Ratio in the Bench Test

2013 ◽  
Vol 457-458 ◽  
pp. 1156-1162 ◽  
Author(s):  
Jian Jun Zhong ◽  
Sheng Nan Fang ◽  
Chang Ying Linghu
Keyword(s):  
Signal To Noise Ratio ◽  
Basis Functions ◽  
Bench Test ◽  
Signal Denoising ◽  
Wavelet Basis ◽  
Signal To Noise ◽  
Denoising Method ◽  
Acceleration Signal ◽  
Noise Ratio ◽  
Filtering Effect

During the tests of the vehicle automatic transmission bench, the acceleration signal is needed to be denoised. As a means of denoising, wavelet threshold denoising method has small amount of calculation and better filtering effect. However, adopting different wavelet basis functions as well as different threshold rules might have a direct effect on the signal denoising. In this paper, we firstly construct the simulated noisy signal approximated to the observed signal, and then do the signal denoising experiment of parameter matching. Secondly, seven Symlets wavelet basis functions and four classical wavelet threshold rules are selected and tested one by one. Signal to noise ratio (SNR) and root mean square error (RMSE) of the denoised signal, the evaluation indicators, are calculated and carried out in accordance with the merits of denoising effect. Thus the optimal combination of the fixed threshold rule and sym8 wavelet basis function is obtained. Finally, this combination is used in the bench test to denoise the angular acceleration signal, and good filtering effect is achieved.

Application of Wavelet Method on Preprocessing of Voltage Signals

2010 ◽  
Vol 40-41 ◽  
pp. 272-276
Author(s):  
Li Di Wang ◽  
Nan Zhu ◽  
Jin Kai Li
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Electrical Parameter ◽  
Wavelet Denoising ◽  
Signal To Noise ◽  
Denoising Method ◽  
Wavelet Method ◽  
Squared Error ◽  
Noise Ratio

Wavelet denoising method is applied in the measurement voltage signals in this paper. Noise reduction is important for signal preprocessing in order to achieve many objects such as the improvement of accuracy of modal analysis and electrical parameter identification, the effective extraction of features and auto-matic classification of different kinds of signals. The voltage signals measured from one 35Kv bus are used for the preprocessing research. The denoising effect is evaluated by three parameters, i.e. signal to noise ratio, mean squared error, and capture ability of step points. Compared with the traditional methods including mean filtering and medial filtering, wavelet method is superior in signal to noise ratio and mean squared error.

scholarly journals Image Denoising Based on Wavelet Transform using Visu Thresholding Technique

2018 ◽  
Vol 3 (4) ◽  
pp. 444-449 ◽  
Author(s):  
Pushpa Koranga ◽  
Garima Singh ◽  
Dikendra Verma ◽  
Shshank Chaube ◽  
Anuj Kumar ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Root Mean Square Error ◽  
Image Denoising ◽  
Environmental Problem ◽  
Signal To Noise Ratio ◽  
Mean Square ◽  
Signal To Noise ◽  
Denoising Method ◽  
Degraded Image

The image often contains noises due to several factors such as a problem in devices or due to an environmental problem etc. Noise is mainly undesired information, which degrades the quality of the picture. Therefore, image denoising method is adopted to remove the noises from the degraded image which in turn improve the quality of the image. In this paper, image denoising has been done by wavelet transform using Visu thresholding techniques for different wavelet families. PSNR (Peak signal to noise ratio) and RMSE (Root Mean Square Error) value is also calculated for different wavelet families.

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