scholarly journals Minimum noiseless description length (MNDL) thresholding

2021 ◽  
Author(s):  
Azadeh Fakhrzadeh
Keyword(s):  
Image Denoising ◽  
Additive Noise ◽  
Random Variable ◽  
Data Driven ◽  
Mean Square ◽  
Chi Square ◽  
Denoising Method ◽  
Thresholding Method ◽  
Simulation Results ◽  
Subspace Selection

In this thesis, the problem of data denoising is considered and a new data denoising method is developed. This approach is an adaptive, data-driven thresholding method that is based on Minimum Noiseless Description Length (MNDL). MNDL is an approach to subspace selection which estimates bounds on the desired Mean Square Error (MSE). The subspace minimizing these bounds is chosen as the optimum one. In this research, we explore application of MNDL Subspace Selection (MNDL-SS) as a thresholding method. Although the basic idea and desired criterion of MNDL thresholding and MNDL-SS are the same, the challenges in calculation of the desired criterion in MNDL thresholding are very different. In MNDL-SS, the additive noise effects are in the form of samples of a Chi-Square random variable. However, this assumption does not hold for MNDL thresholding anymore. In this research, we developed a new method for calculation of the desired criterion based on characteristics of noise in thresholding. Our simulation results show that MNDL thresholding outperforms the compared methods. In this thesis, we also explore the area of image denoising. In image denoising approaches, some properties of the image are considered. One of the well known image denoising methods, that outperforms other methods, is BayesShrink. We compare our method with BayesShrink. We show that the results of MNDS thresholding are comparable with BayesShrink in our simulations.

scholarly journals Minimum noiseless description length (MNDL) thresholding

2021 ◽  
Author(s):  
Azadeh Fakhrzadeh
Keyword(s):  
Image Denoising ◽  
Additive Noise ◽  
Random Variable ◽  
Data Driven ◽  
Mean Square ◽  
Chi Square ◽  
Denoising Method ◽  
Thresholding Method ◽  
Simulation Results ◽  
Subspace Selection

In this thesis, the problem of data denoising is considered and a new data denoising method is developed. This approach is an adaptive, data-driven thresholding method that is based on Minimum Noiseless Description Length (MNDL). MNDL is an approach to subspace selection which estimates bounds on the desired Mean Square Error (MSE). The subspace minimizing these bounds is chosen as the optimum one. In this research, we explore application of MNDL Subspace Selection (MNDL-SS) as a thresholding method. Although the basic idea and desired criterion of MNDL thresholding and MNDL-SS are the same, the challenges in calculation of the desired criterion in MNDL thresholding are very different. In MNDL-SS, the additive noise effects are in the form of samples of a Chi-Square random variable. However, this assumption does not hold for MNDL thresholding anymore. In this research, we developed a new method for calculation of the desired criterion based on characteristics of noise in thresholding. Our simulation results show that MNDL thresholding outperforms the compared methods. In this thesis, we also explore the area of image denoising. In image denoising approaches, some properties of the image are considered. One of the well known image denoising methods, that outperforms other methods, is BayesShrink. We compare our method with BayesShrink. We show that the results of MNDS thresholding are comparable with BayesShrink in our simulations.

Improved Wavelet Thresholding for CCD Measurement Image Denoising

2014 ◽  
Vol 574 ◽  
pp. 432-435 ◽  
Author(s):  
Jie Zhan ◽  
Zhen Xing Li
Keyword(s):  
Image Denoising ◽  
Threshold Function ◽  
Wavelet Thresholding ◽  
Wavelet Coefficients ◽  
Local Correlation ◽  
Thresholding Method ◽  
Threshold Functions ◽  
Soft Threshold ◽  
Simulation Results ◽  
Filtering Effect

An improved wavelet thresholding method is presented and successfully applied to CCD measuring image denoising. On the analysis of the current widely used soft threshold and hard threshold, combining characteristics of the CCD measuring image and use of local correlation of wavelet coefficients, an improved threshold function is proposed, and the denoising results were contrasted among different threshold functions. The simulation results show that adopting the improved threshold function can acquire better filtering effect than traditional soft threshold and hard threshold methods.

scholarly journals Image Denoising Based on Wavelet Transform using Visu Thresholding Technique

2018 ◽  
Vol 3 (4) ◽  
pp. 444-449 ◽  
Author(s):  
Pushpa Koranga ◽  
Garima Singh ◽  
Dikendra Verma ◽  
Shshank Chaube ◽  
Anuj Kumar ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Root Mean Square Error ◽  
Image Denoising ◽  
Environmental Problem ◽  
Signal To Noise Ratio ◽  
Mean Square ◽  
Signal To Noise ◽  
Denoising Method ◽  
Degraded Image

The image often contains noises due to several factors such as a problem in devices or due to an environmental problem etc. Noise is mainly undesired information, which degrades the quality of the picture. Therefore, image denoising method is adopted to remove the noises from the degraded image which in turn improve the quality of the image. In this paper, image denoising has been done by wavelet transform using Visu thresholding techniques for different wavelet families. PSNR (Peak signal to noise ratio) and RMSE (Root Mean Square Error) value is also calculated for different wavelet families.

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.

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