An image Denoising Method Based On Multi Resulation Bilateral Filter

In all the instances of image acquisition, transmission and storage, the unwanted noise gets into the information content of the image and thereby introduces an unpleasant visual quality to the observer. So the field of image processing has produced a lot of image denoising algorithms and techniques to improve the visual quality of the image. Since noise cannot be reduced to zero practically, the need for faithful and efficient denoising techniques to produce almost noiseless images demands a systematic research work in the field of denoising methods. The denoising process using a bilateral filter even though produces improvement in the image quality, it does not show consistency when the noise level is high and also the peak signal to noise ratio (PSNR) and Image quality Index (IQI) do not show any improvement. This paper proposes an improved algorithm that incorporates the function of bilateral filter model and wavelet thresholding using Neighshrink SURE method. The results show significant improvement in both PSNR and IQI values with respect to the four standard test images under various noise conditions.

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A Non-Reference Image Denoising Method for Infrared Thermal Image Based on Enhanced Dual-Tree Complex Wavelet Optimized by Fruit Fly Algorithm and Bilateral Filter

Applied Sciences ◽

10.3390/app7111190 ◽

2017 ◽

Vol 7 (11) ◽

pp. 1190 ◽

Cited By ~ 6

Author(s):

Yiwen Liu ◽

Zhongbin Wang ◽

Lei Si ◽

Lin Zhang ◽

Chao Tan ◽

...

Keyword(s):

Image Denoising ◽

Bilateral Filter ◽

Fruit Fly ◽

Thermal Image ◽

Reference Image ◽

Denoising Method ◽

Complex Wavelet

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A Power Thresholding Function-based Wavelet Image Denoising Method

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2018.62.1.010506 ◽

2018 ◽

Vol 62 (1) ◽

pp. 105061-1050611 ◽

Cited By ~ 1

Author(s):

Zhidan Yan ◽

Wenyi Xu ◽

Chunmei Yang

Keyword(s):

Image Denoising ◽

Denoising Method ◽

Wavelet Image ◽

Thresholding Function

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Sonar image denoising method based on NSCT circle sample

Journal of Computer Applications ◽

10.3724/sp.j.1087.2009.00068 ◽

2009 ◽

Vol 29 (1) ◽

pp. 68-70

Author(s):

Chun-rui TANG ◽

Dan-dan LIU

Keyword(s):

Image Denoising ◽

Denoising Method ◽

Sonar Image

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Improved anisotropic diffusion ultrasound image denoising method based on logarithmic compression

Journal of Computer Applications ◽

10.3724/sp.j.1087.2012.03218 ◽

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

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

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-02013-2 ◽

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.

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

Energies ◽

10.3390/en14113267 ◽

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.

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