An Efficient Random Valued Impulse Noise Suppression Technique Using Artificial Neural Network and Non-Local Mean Filter

2022 ◽  
pp. 1157-1173
Author(s):  
Bibekananda Jena ◽  
Punyaban Patel ◽  
G.R. Sinha
Keyword(s):  
Neural Network ◽  
Impulse Noise ◽  
Noise Suppression ◽  
Back Propagation ◽  
Feed Forward Neural Network ◽  
Suppression Technique ◽  
Mean Filter ◽  
Local Mean ◽  
Non Local ◽  
A New Technique

A new technique for suppression of Random valued impulse noise from the contaminated digital image using Back Propagation Neural Network is proposed in this paper. The algorithms consist of two stages i.e. Detection of Impulse noise and Filtering of identified noisy pixels. To classify between noisy and non-noisy element present in the image a feed-forward neural network has been trained with well-known back propagation algorithm in the first stage. To make the detection method more accurate, Emphasis has been given on selection of proper input and generation of training patterns. The corrupted pixels are undergoing non-local mean filtering employed in the second stage. The effectiveness of the proposed technique is evaluated using well known standard digital images at different level of impulse noise. Experiments show that the method proposed here has excellent impulse noise suppression capability.

An Efficient Random Valued Impulse Noise Suppression Technique Using Artificial Neural Network and Non-Local Mean Filter

2018 ◽  
Vol 5 (2) ◽  
pp. 148-163 ◽  
Author(s):  
Bibekananda Jena ◽  
Punyaban Patel ◽  
G.R. Sinha
Keyword(s):  
Neural Network ◽  
Impulse Noise ◽  
Noise Suppression ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Suppression Technique ◽  
Local Mean ◽  
Non Local

A new technique for suppression of Random valued impulse noise from the contaminated digital image using Back Propagation Neural Network is proposed in this paper. The algorithms consist of two stages i.e. Detection of Impulse noise and Filtering of identified noisy pixels. To classify between noisy and non-noisy element present in the image a feed-forward neural network has been trained with well-known back propagation algorithm in the first stage. To make the detection method more accurate, Emphasis has been given on selection of proper input and generation of training patterns. The corrupted pixels are undergoing non-local mean filtering employed in the second stage. The effectiveness of the proposed technique is evaluated using well known standard digital images at different level of impulse noise. Experiments show that the method proposed here has excellent impulse noise suppression capability.

A New Technique to Improve Estimation of Position for Serial Robots

2014 ◽  
Vol 511-512 ◽  
pp. 817-821
Author(s):  
Ya Li Wang ◽  
Zhen Zhong Wei ◽  
Ming Wei Shao ◽  
Guang Jun Zhang
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Joint Angles ◽  
Feed Forward Neural Network ◽  
Polynomial Sequence ◽  
Serial Robots ◽  
Network Method ◽  
A New Technique ◽  
Relative Errors ◽  
The Given

This paper presents a new method to improve the estimation of the positions for serial robots using power-activated feed-forward neural network. In the paper, a six-input three-output neural network is created with robot joint angle sine values as inputs and positions in the world frame as outputs. The neuron is activated with an orthogonal polynomial sequence,and the neural weights can be calculated directly without involving iterative and convergent problem. It is found that, the RMS error is less than 0.25 mm for the whole work space. And the absolute and relative errors of this method are smaller than those of built in kinematics model and the traditional back propagation (BP) neural network method. Experimental results show that the proposed method can effectively predict the positioning of the given joint angles.

scholarly journals Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 827 ◽  
Author(s):  
Chundi Jiang ◽  
Wei Yang ◽  
Yu Guo ◽  
Fei Wu ◽  
Yinggan Tang
Keyword(s):  
Image Segmentation ◽  
Relative Entropy ◽  
Spatial Information ◽  
Two Dimensional ◽  
Nonlocal Means ◽  
Mean Filter ◽  
Filter Process ◽  
Local Mean ◽  
Non Local ◽  
Correlation Information

Spatial correlation information between pixels is considered to be very important in thresholding methods. However, it is often ignored and thus unsatisfied segmentation results maybe obtained. To overcome this shortcoming, we propose a new image segmentation approach by taking not only pixels’ spatial information but also pixels’s gray level into account. First, a non-local mean filter is imposed on the image. Then the filtered image and the original image together are adopted to build a two dimensional histogram, it is called non-local mean two dimensional histogram. Finally, a minimum relative entropy criteria is used to select the ideal thresholding vector. Since the non-local mean filter process is performed in a neighborhood of current pixel, it carries out the spatial information of current pixel. Segmentation results on several images illustrate the effectiveness of the proposed thresholding method, whose segmentation accuracy are greatly improved compared to most existing thresholding methods.

scholarly journals Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image

2016 ◽  
Vol 195 ◽  
pp. 88-95 ◽  
Author(s):  
Jian Yang ◽  
Jingfan Fan ◽  
Danni Ai ◽  
Xuehu Wang ◽  
Yongchang Zheng ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Medical Ultrasound ◽  
Local Statistics ◽  
Mean Filter ◽  
Local Mean ◽  
Non Local ◽  
Speckle Noise Reduction

An Efficient Neural Network Model for De-noising of MEMS-Based Inertial Data

2004 ◽  
Vol 57 (3) ◽  
pp. 407-415 ◽  
Author(s):  
Ahmed El-Rabbany ◽  
Mohammed El-Diasty
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Noise Suppression ◽  
Back Propagation ◽  
Frequency Noise ◽  
Original Signal ◽  
Back Propagation Algorithm ◽  
Major Drawback ◽  
Noise Component

Micro-Electro-Mechanical System (MEMS)-based inertial technology has recently evolved. It holds remarkable potential as the future technology for various navigation related applications. This is mainly due to the significant reduction in size, cost, and weight of MEMS sensors. A major drawback of low-cost MEMS-based inertial sensors, however, is that their output signals are contaminated by high-level noise. Unless the high frequency noise component is suppressed, optimizing the pre-filtering methodology cannot be achieved. This paper proposes a neural network-based de-noising model for MEMS-based inertial data. A modular, three-layer feedforward neural network trained using the back-propagation algorithm is used for this purpose. Simulated and real MEMS-based inertial data sets are used to validate the model. It is shown that the model is capable of reducing the noise of the Crossbow's AHRS300CA IMU data by over one order of magnitude without altering the stochastic nature of the original signal. This is of utmost importance in developing a generic stochastic model for MEMS-based inertial data. A comparison between the developed neural network model and the wavelet de-noising method is made to further validate the model. It is shown that achieving the same level of noise suppression with wavelet-based de-noising model changes the stochastic characteristics of original signal.

scholarly journals Modeling of Speech Recognition Using Artificial Neural Network

2019 ◽  
Vol 8 (9) ◽  
pp. 1225-1229
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Pattern Matching ◽  
Back Propagation ◽  
Experimental Result ◽  
Communication Link ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
The Voice

Automatic speech recognition has attained a lot of significance as it can act as easy communication link between machines and humans. This mode of communication is easy for man to use as it is effortless and easy. Many approaches for extraction of the features of the speech and classification of speech have been considered. This paper unveils the importance of neutral network and the way it can be used for recognition of speech. Mel Frequency Cepstrum Coefficients is made use of for extraction of the features from the voice. For pattern matching neural network has been used. MATLAB has been used to show how the speech is recognized. In this paper the speech recognition has been done firstly by multilayer feed forward neural network using Back propagation algorithm. Then the process of speech recognition is shown by using Radial basis function neural network. The paper then analyzes the performance of both the algorithms and experimental result shows that BPNN outperforms the RBFNN.

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