scholarly journals Iterative based image and video denoising by fractional block matching and transform domain filtering

2021 ◽  
Author(s):  
Amir Mehdizadeh Hemat Abadi ◽  
Mohammad Reza Hosseiny Fatemi
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Weighted Average ◽  
Block Matching ◽  
Objective Measures ◽  
Transform Domain ◽  
Video Denoising ◽  
Simulation Results ◽  
Fractional Motion Estimation

This paper presents an iterative algorithm for image and video denoising which is based on fractional block-matching and transform domain filtering. We propose fractional motion estimation technique to find the most accurate similar blocks for each block of an image which improves sparsity enabling effective image denoising. By taking the advantage of blocks similarity and wavelet transform domain filtering along with weighted average function (WAF) in an iterative based manner, we achieve a higher level of sparsity and a better exploiting of blocks similarity redundancies of noisy images that increase the chance of preserving details and edges in the restored image. Since our algorithm is iterative, we can tradeoff between image denoising degree and computational complexity. In addition, we develop a video denoising algorithm based on the proposed image denoising algorithm. The simulation results of images and videos contaminated by additive white Gaussian noise demonstrate that our algorithm substantially achieves better denoising performance compared with previously published algorithms in terms of subjective and objective measures.

scholarly journals Iterative based image and video denoising by fractional block matching and transform domain filtering

2021 ◽  
Author(s):  
Amir Mehdizadeh Hemat Abadi ◽  
Mohammad Reza Hosseiny Fatemi
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Weighted Average ◽  
Block Matching ◽  
Objective Measures ◽  
Transform Domain ◽  
Video Denoising ◽  
Simulation Results ◽  
Fractional Motion Estimation

This paper presents an iterative algorithm for image and video denoising which is based on fractional block-matching and transform domain filtering. We propose fractional motion estimation technique to find the most accurate similar blocks for each block of an image which improves sparsity enabling effective image denoising. By taking the advantage of blocks similarity and wavelet transform domain filtering along with weighted average function (WAF) in an iterative based manner, we achieve a higher level of sparsity and a better exploiting of blocks similarity redundancies of noisy images that increase the chance of preserving details and edges in the restored image. Since our algorithm is iterative, we can tradeoff between image denoising degree and computational complexity. In addition, we develop a video denoising algorithm based on the proposed image denoising algorithm. The simulation results of images and videos contaminated by additive white Gaussian noise demonstrate that our algorithm substantially achieves better denoising performance compared with previously published algorithms in terms of subjective and objective measures.

scholarly journals BER Performance Analysis of Non-Coherent Q-Ary Pulse Position Modulation Receivers on AWGN Channel

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6102
Author(s):  
Xianhua Shi ◽  
Yimao Sun ◽  
Jie Tian ◽  
Maolin Chen ◽  
Youjiang Liu ◽  
...  
Keyword(s):  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Theoretical Formula ◽  
Pulse Position Modulation ◽  
Error Performance ◽  
Optimal Receiver ◽  
Awgn Channel ◽  
Simulation Results ◽  
The Relationship ◽  
Better Than

This paper introduces the structure of a Q-ary pulse position modulation (PPM) signal and presents a noncoherent suboptimal receiver and a noncoherent optimal receiver. Aiming at addressing the lack of an accurate theoretical formula of the bit error rate (BER) of a Q-ary PPM receiver in the additive white Gaussian noise (AWGN) channel in the existing literature, the theoretical formulas of the BER of a noncoherent suboptimal receiver and noncoherent optimal receiver are derived, respectively. The simulation results verify the correctness of the theoretical formulas. The theoretical formulas can be applied to a Q-ary PPM system including binary PPM. In addition, the analysis shows that the larger the Q, the better the error performance of the receiver and that the error performance of the optimal receiver is about 2 dB better than that of the suboptimal receiver. The relationship between the threshold coefficient of the suboptimal receiver and the error performance is also given.

scholarly journals Performance Comparison of Digitized Quaternion Modulation with other Modulation Schemes

2019 ◽  
Vol 27 ◽  
pp. 01004
Author(s):  
Anam Zahra ◽  
Qasim Umar Khan
Keyword(s):  
Global Positioning System ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Performance Comparison ◽  
Superior Performance ◽  
Positioning System ◽  
Space Communication ◽  
Global Positioning ◽  
Simulation Results ◽  
Modulation Schemes

In wireless networks signal’s security from noise has been a very challenging issue, primarily because of the broadcast nature of communication. This paper focuses on digitized Quaternion Modulation (QM) which gives better performance as compared to QPSK, QAM and QFSK. We compare the performance of quaternion modulation with other modulation schemes in terms of BER using idealistic Additive White Gaussian Noise AWGN channel. This scheme can be used in applications such as Global Positioning System (GPS), satellite and space communication system to reduce errors. The simulation results show superior performance of the proposed digitized Quaternion Modulation over its counterparts. Thus one may trade off bandwidth for BER.

Parameter Estimation of Co-Channel AIS Signal Using Ambiguity Function

2014 ◽  
Vol 644-650 ◽  
pp. 4035-4039
Author(s):  
Hao Su Zhou ◽  
Jian Xin Wang
Keyword(s):  
Parameter Estimation ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Reference Signal ◽  
Two Dimensions ◽  
Ambiguity Function ◽  
Weak Signal ◽  
Strong Signal ◽  
Estimated Parameters ◽  
Simulation Results

A new data-aided algorithm for parameter estimation of the co-channel AIS signal transmitted over the additive white Gaussian noise channel is proposed in this paper. The co-channel signal consists of a strong signal with high power and a weak signal with low power. The parameters of the strong signal are estimated by searching the ambiguity function of the co-channel signal in two dimensions. A reference signal is therefore reconstructed with the estimated parameters and the aided data. By removing the ambiguity function of the reconstructed reference signal from that of the original co-channel signal, a new co-channel signal ambiguity function is obtained, from which the parameters of the weak signal are estimated. The simulation results illustrate that the proposed algorithm can estimate the parameters of the co-channel AIS signal effectively.

Research on Overlapped Time Division Multiplexing in the Amplify-and-Forward Cooperative Mode

2015 ◽  
Vol 713-715 ◽  
pp. 1103-1106
Author(s):  
Hong Zhang ◽  
Wei Ping Ma
Keyword(s):  
Cooperative Communication ◽  
Gaussian Noise ◽  
Communication System ◽  
Additive White Gaussian Noise ◽  
Spectrum Efficiency ◽  
Amplify And Forward ◽  
Time Division Multiplexing ◽  
Simulation Results ◽  
Time Division ◽  
New System

In order to improve spectrum efficiency of cooperative communication system, Overlapped Time Division Multiplexing (OVTDM) is applied to Amplify-and-Forward (AF) cooperative communication system. The simulation results under Additive White Gaussian Noise (AWGN) channel show that the performance of new system is superior to that of corresponding AF cooperative communication system.

Image denoising via Bayesian estimation of local variance with Maxwell density prior

2015 ◽  
Vol 06 (02) ◽  
pp. 1550002
Author(s):  
Pichid Kittisuwan
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Prior Distribution ◽  
Additive White Gaussian Noise ◽  
Maximum A Posteriori ◽  
Map Estimation ◽  
A Posteriori ◽  
Bayesian Techniques ◽  
Local Variance ◽  
Selection Of

The need for efficient image denoising methods has grown with the massive production of digital images and movies of all kinds. The distortion of images by additive white Gaussian noise (AWGN) is common during its processing and transmission. This paper is concerned with dual-tree complex wavelet-based image denoising using Bayesian techniques. Indeed, one of the cruxes of the Bayesian image denoising algorithms is to estimate the local variance of the image. Here, we employ maximum a posteriori (MAP) estimation to calculate local observed variance with Maxwell density prior for local observed variance and Gaussian distribution for noisy wavelet coefficients. Evidently, our selection of prior distribution is motivated by analytical and computational tractability. The experimental results show that the proposed method yields good denoising results.

A Non-Local Image Denoising Technique Using Adaptive Filter Parameter

2014 ◽  
Vol 556-562 ◽  
pp. 4839-4842
Author(s):  
Song Yuan Tang
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Wiener Filter ◽  
White Gaussian Noise ◽  
Filter Parameter ◽  
Iterative Computation ◽  
Local Mean ◽  
Non Local

This paper proposes a method to obtain the optimal filter parameter of the non-local mean (NLM) algorithm. The parameter is assumed to be a function of the variance of the additive white Gaussian noise and is adaptive estimated. The initialization of the variance of the additive white Gaussian noise is estimated by Wiener filter. Then the NLM filter is used to adaptively estimate the noise variance. The image denoising is an iterative computation till the parameter convergence. Experiments show that the proposed method can improve the quality of the denoised images efficiently.

scholarly journals Convergence analysis of the unpunctured turbo trellis-coded modulation (UTTCM)

2019 ◽  
Vol 13 (2) ◽  
pp. 447
Author(s):  
Ahmed Haffane ◽  
Abdelhafid Hasni ◽  
Mustapha Khelifi ◽  
Boufeldja Kadri
Keyword(s):  
Gaussian Noise ◽  
Iterative Decoding ◽  
Information Transfer ◽  
Additive White Gaussian Noise ◽  
Coded Modulation ◽  
Extrinsic Information ◽  
Trellis Coded Modulation ◽  
Exit Chart ◽  
Trellis Coded ◽  
Simulation Results

In this paper, the performance of the Unpunctured Turbo Trellis-Coded Modulation (UTTCM) over Additive White Gaussian Noise (AWGN) channel is analyzed using the non-binary extrinsic information transfer (EXIT) chart. The exchange of the extrinsic information between the decoder components is tracked, allowing the generation of an EXIT chart, which is a powerful tool for analyzing the convergence behavior of iterative decoding and prediction of convergence position. The Simulation results are compared with the turbo cliff positions on the BER curves.

scholarly journals EDGE–PRESERVING DENOISING BASED ON DYNAMIC PROGRAMMING ON THE FULL SET OF ADJACENCY GRAPHS

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 55-60
Author(s):  
P. C. Thang ◽  
A. V. Kopylov ◽  
S. D. Dvoenko
Keyword(s):  
Dynamic Programming ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Medical Diagnosis ◽  
Additive White Gaussian Noise ◽  
Experimental Results ◽  
Crucial Importance ◽  
High Quality ◽  
Edge Preserving ◽  
Adjacency Graphs

The ability of a denoising procedure to preserve fine image structures when suppressing unwanted noise has crucial importance for an accurate and effective medical diagnosis. We introduce here a new procedure of edge-preserving denoising for medical images, that combines the flexibility in prior assumptions, and computational effectiveness of parametric multi-quadratic dynamic programming with the increased accuracy of a tree-like representation of a discrete lattice based on the full set of possible adjacency graphs of image elements. Proposed procedure can effectively remove an additive white Gaussian noise with high quality. We provide experimental results in image denoising as well as comparison with related methods.

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