Image Denoising Method Based on v-Support Vector Regression and Noise Detection

2013 ◽  
Vol 756-759 ◽  
pp. 4126-4132
Author(s):  
Chang You Wang ◽  
Zhao Long Gao
Keyword(s):  
Support Vector Regression ◽  
Median Filter ◽  
Visual Quality ◽  
Decision Function ◽  
Support Vector ◽  
Noise Detection ◽  
Training Set ◽  
Denoising Method ◽  
Mean Filter ◽  
Filtering Effect

Aimed at the correlation between noise pixels and neighboring pixels, a new method based on the-support vector regression (-SVR) is proposed to remove the salt & pepper noise in corrupted images. The new algorithm first takes a decision whether the pixel under test is noise or not by comparing the block uniformity of the 3x3 window with one of the entire image, secondly adjusts adaptively the size of filtering window which is used to determine the training set according to the number of noise points in the window, thirdly determines the decision function that is used to predict the gray value of the noise pixels by means of training set, finally removes the noises in terms of the decision function based on-SVR. Experimental results clearly indicate that the proposed method has a better filtering effect than the existing methods such as standard mean filter, standard median filter, adaptive median filter by means of visual quality and quanti-tative measures.

scholarly journals New Strategy for Analog Circuit Performance Evaluation under Disturbance and Fault Value

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Aihua Zhang ◽  
Yongchao Wang ◽  
Chen Chen ◽  
Hamid Reza Karimi
Keyword(s):  
Performance Evaluation ◽  
Support Vector Regression ◽  
Analog Circuit ◽  
Support Vector ◽  
Training Set ◽  
Circuit Performance ◽  
New Strategy ◽  
Indirect Evaluation ◽  
Novel Strategy ◽  
Fuzzy Learning

Focus on this issue of disturbance and fault value is inevitable in data collection about analog circuit. A novel strategy is developed for analog circuit online performance evaluation based on fuzzy learning and double weighted support vector machine (DWMK-FSVM). First, the double weighted support vector regression machine is employed to be the indirect evaluation means, relied on the college analog electronic technology experiment to evaluate analog circuit. Second, the superiority of fuzzy learning also is addressed to realize active suppression to the fault values and disturbance parameters. Moreover, the multikernel RBF is employed by support vector regression machine to realize more flexibility online such as the bandwidths tuning. Numerical results, supported by the college analog circuit experiments, adopted OTL performance eight indexes, which were obtained via precision instrument evaluation in two years to construct training set and are then to be evaluated online based on DWMK-FSVM. Simulation results presented not only highlight precision of the evaluation strategy derived here but also illustrate its great robustness.

scholarly journals Image Noise Removal Method Based on Improved Nonlocal Mean Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Keya Huang ◽  
Hairong Zhu
Keyword(s):  
Signal To Noise Ratio ◽  
Median Filter ◽  
Visual Quality ◽  
Noise Removal ◽  
Interactive Systems ◽  
Image Processing Algorithm ◽  
Signal To Noise ◽  
Adaptive Median Filter ◽  
Mixed Noise ◽  
Filtering Effect

Aiming at the problem of unclear images acquired in interactive systems, an improved image processing algorithm for nonlocal mean denoising is proposed. This algorithm combines the adaptive median filter algorithm with the traditional nonlocal mean algorithm, first adjusts the image window adaptively, selects the corresponding pixel weight, and then denoises the image, which can have a good filtering effect on the mixed noise. The experimental results show that, compared with the traditional nonlocal mean algorithm, the algorithm proposed in this paper has better results in the visual quality and peak signal-to-noise ratio (PSNR) of complex noise images.

An Alternative Approach to Prediction of Critical Heat Flux: Projection Support Vector Regression

2014 ◽  
Author(s):  
Botao Jiang ◽  
Fuyu Zhao
Keyword(s):  
Heat Flux ◽  
Support Vector Regression ◽  
Critical Heat Flux ◽  
Cooling System ◽  
Prediction Method ◽  
Training Data ◽  
Support Vector ◽  
Training Set ◽  
Test Set ◽  
Alternative Approach

Critical heat flux (CHF) is one of the most crucial design criteria in other boiling systems such as evaporator, steam generators, fuel cooling system, boiler, etc. This paper presents an alternative CHF prediction method named projection support vector regression (PSVR), which is a combination of feature vector selection (FVS) method and support vector regression (SVR). In PSVR, the FVS method is first used to select a relevant subset (feature vectors, FVs) from the training data, and then both the training data and the test data are projected into the subspace constructed by FVs, and finally SVR is applied to estimate the projected data. An available CHF dataset taken from the literature is used in this paper. The CHF data are split into two subsets, the training set and the test set. The training set is used to train the PSVR model and the test set is then used to evaluate the trained model. The predicted results of PSVR are compared with those of artificial neural networks (ANNs). The parametric trends of CHF are also investigated using the PSVR model. It is found that the results of the proposed method not only fit the general understanding, but also agree well with the experimental data. Thus, PSVR can be used successfully for prediction of CHF in contrast to ANNs.

scholarly journals SNOW DEPTH ESTIMATION USING TIME SERIES PASSIVE MICROWAVE IMAGERY VIA GENETICALLY SUPPORT VECTOR REGRESSION (CASE STUDY URMIA LAKE BASIN)

2015 ◽  
Vol XL-1-W5 ◽  
pp. 555-558 ◽  
Author(s):  
N. Zahir ◽  
H. Mahdi
Keyword(s):  
Support Vector Regression ◽  
Snow Depth ◽  
Depth Estimation ◽  
Statistical Problem ◽  
Least Square ◽  
Support Vector ◽  
Lake Basin ◽  
Lake Urmia ◽  
Denoising Method ◽  
Sd Model

Lake Urmia is one of the most important ecosystems of the country which is on the verge of elimination. Many factors contribute to this crisis among them is the precipitation, paly important roll. Precipitation has many forms one of them is in the form of snow. The snow on Sahand Mountain is one of the main and important sources of the Lake Urmia’s water. Snow Depth (SD) is vital parameters for estimating water balance for future year. In this regards, this study is focused on SD parameter using Special Sensor Microwave/Imager (SSM/I) instruments on board the Defence Meteorological Satellite Program (DMSP) F16. The usual statistical methods for retrieving SD include linear and non-linear ones. These methods used least square procedure to estimate SD model. Recently, kernel base methods widely used for modelling statistical problem. From these methods, the support vector regression (SVR) is achieved the high performance for modelling the statistical problem. Examination of the obtained data shows the existence of outlier in them. For omitting these outliers, wavelet denoising method is applied. After the omission of the outliers it is needed to select the optimum bands and parameters for SVR. To overcome these issues, feature selection methods have shown a direct effect on improving the regression performance. We used genetic algorithm (GA) for selecting suitable features of the SSMI bands in order to estimate SD model. The results for the training and testing data in Sahand mountain is [R²_TEST=0.9049 and RMSE= 6.9654] that show the high SVR performance.

Blind 3D mesh visual quality assessment using support vector regression

2018 ◽  
Vol 77 (18) ◽  
pp. 24365-24386 ◽  
Author(s):  
Ilyass Abouelaziz ◽  
Mohammed El Hassouni ◽  
Hocine Cherifi
Keyword(s):  
Quality Assessment ◽  
Support Vector Regression ◽  
Visual Quality ◽  
Support Vector ◽  
3D Mesh ◽  
Visual Quality Assessment

Comparison between Mean Filter and Median Filter Algorithm in Image Denoising Field

2014 ◽  
Vol 644-650 ◽  
pp. 4112-4116 ◽  
Author(s):  
Xiao Xin Sun ◽  
Wei Qu
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Response Characteristic ◽  
Fast Response ◽  
High Signal ◽  
Signal To Noise ◽  
Denoising Method ◽  
Mean Filter ◽  
Noise Ratio

An image denoising method based on spatial filtering is proposed on order to overcoming the shortcomings of traditional denoising methods in this paper. The method combined mean mask algorithm with median filtering technique is able to replace the gray values of noisy image pixel by the mean or median value in its neighborhood mask matrix and highlight the characteristic value of the image. Peak signal to noise ratio and mean square error are used as the evaluation index in this method and comparison between mean filter and median filter is done. The experimental results show that this denoising system makes the images have a high signal to noise ratio and integrity of edge details and take into account real-time, and fast response characteristic of the system.

Accurate On-line Support Vector Regression

2003 ◽  
Vol 15 (11) ◽  
pp. 2683-2703 ◽  
Author(s):  
Junshui Ma ◽  
James Theiler ◽  
Simon Perkins
Keyword(s):  
Support Vector Regression ◽  
Numerical Experiments ◽  
Cross Validation ◽  
Classification Algorithm ◽  
Support Vector ◽  
Training Set ◽  
Warm Start ◽  
On Line ◽  
Line Setting

Batch implementations of support vector regression (SVR) are inefficient when used in an on-line setting because they must be retrained from scratch every time the training set is modified. Following an incremental support vector classification algorithm introduced by Cauwenberghs and Poggio (2001), we have developed an accurate on-line support vector regression (AOSVR) that efficiently updates a trained SVR function whenever a sample is added to or removed from the training set. The updated SVR function is identical to that produced by a batch algorithm. Applications of AOSVR in both on-line and cross-validation scenarios are presented.Inbothscenarios, numerical experiments indicate that AOSVR is faster than batch SVR algorithms with both cold and warm start.

Sign in / Sign up

Export Citation Format

Share Document