An Improved SAR Image Denoising Method Based on Bootstrap Statistical Estimation with ICA Basis

2016 ◽  
Vol 25 (4) ◽  
pp. 786-792
Author(s):  
Jian Ji ◽  
Yang Li
Keyword(s):  
Image Denoising ◽  
Statistical Estimation ◽  
Sar Image ◽  
Denoising Method

scholarly journals SAR image denoising method based on sparse representation

2019 ◽  
Vol 2019 (20) ◽  
pp. 7153-7156 ◽  
Author(s):  
Hao-Tian Zhou ◽  
Liang Chen ◽  
Bo Fu ◽  
Hao Shi
Keyword(s):  
Sparse Representation ◽  
Image Denoising ◽  
Sar Image ◽  
Denoising Method

An image Denoising Method Based On Multi Resulation Bilateral Filter

2018 ◽  
Vol 6 (12) ◽  
pp. 448-452
Author(s):  
Md Shaiful Islam Babu ◽  
Kh Shaikh Ahmed ◽  
Md Samrat Ali Abu Kawser ◽  
Ajkia Zaman Juthi
Keyword(s):  
Image Denoising ◽  
Bilateral Filter ◽  
Denoising Method

Sonar image denoising method based on NSCT circle sample

2009 ◽  
Vol 29 (1) ◽  
pp. 68-70
Author(s):  
Chun-rui TANG ◽  
Dan-dan LIU
Keyword(s):  
Image Denoising ◽  
Denoising Method ◽  
Sonar Image

Improved anisotropic diffusion ultrasound image denoising method based on logarithmic compression

2013 ◽  
Vol 32 (11) ◽  
pp. 3218-3220
Author(s):  
Jin YANG ◽  
Zhi-qin LIU ◽  
Yao-bin WANG ◽  
Xiao-ming GAO
Keyword(s):  
Image Denoising ◽  
Anisotropic Diffusion ◽  
Ultrasound Image ◽  
Denoising Method

scholarly journals An image denoising method based on BP neural network optimized by improved whale optimization algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunzhi Wang ◽  
Min Li ◽  
Ruoxi Wang ◽  
Han Yu ◽  
Shuping Wang
Keyword(s):  
Neural Network ◽  
Image Denoising ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Weight Coefficient ◽  
Whale Optimization Algorithm ◽  
Denoising Method ◽  
Training Time ◽  
Whale Optimization ◽  
City Construction

AbstractAs an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.

scholarly journals Novel Features and PRPD Image Denoising Method for Improved Single-Source Partial Discharges Classification in On-Line Hydro-Generators

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3267
Author(s):  
Ramon C. F. Araújo ◽  
Rodrigo M. S. de Oliveira ◽  
Fernando S. Brasil ◽  
Fabrício J. B. Barros
Keyword(s):  
Image Denoising ◽  
Recognition Performance ◽  
Partial Discharge ◽  
Classification Performance ◽  
Partial Discharges ◽  
Denoising Method ◽  
Internal Parameters ◽  
On Line ◽  
Automatic Removal ◽  
The Impact

In this paper, a novel image denoising algorithm and novel input features are proposed. The algorithm is applied to phase-resolved partial discharge (PRPD) diagrams with a single dominant partial discharge (PD) source, preparing them for automatic artificial-intelligence-based classification. It was designed to mitigate several sources of distortions often observed in PRPDs obtained from fully operational hydroelectric generators. The capabilities of the denoising algorithm are the automatic removal of sparse noise and the suppression of non-dominant discharges, including those due to crosstalk. The input features are functions of PD distributions along amplitude and phase, which are calculated in a novel way to mitigate random effects inherent to PD measurements. The impact of the proposed contributions was statistically evaluated and compared to classification performance obtained using formerly published approaches. Higher recognition rates and reduced variances were obtained using the proposed methods, statistically outperforming autonomous classification techniques seen in earlier works. The values of the algorithm’s internal parameters are also validated by comparing the recognition performance obtained with different parameter combinations. All typical PD sources described in hydro-generators PD standards are considered and can be automatically detected.

scholarly journals A New Soft-Thresholding Image Denoising Method

2012 ◽  
Vol 6 ◽  
pp. 10-15 ◽  
Author(s):  
Mantosh Biswas ◽  
Hari Om
Keyword(s):  
Image Denoising ◽  
Denoising Method ◽  
Soft Thresholding
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