scholarly journals External Filtering and Wavelet Domain Thresholding-based Denoising Method for AWGN corrupted images

2021 ◽  
Vol 2 (2) ◽  
Keyword(s):  
Wavelet Domain ◽  
Denoising Method

scholarly journals Image Denoising Using Singular Value Difference in the Wavelet Domain

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Min Wang ◽  
Wei Yan ◽  
Shudao Zhou
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Singular Values ◽  
Singular Value ◽  
Wavelet Domain ◽  
Noisy Image ◽  
Denoising Method ◽  
Diagonal Part

Singular value (SV) difference is the difference in the singular values between a noisy image and the original image; it varies regularly with noise intensity. This paper proposes an image denoising method using the singular value difference in the wavelet domain. First, the SV difference model is generated for different noise variances in the three directions of the wavelet transform and the noise variance of a new image is used to make the calculation by the diagonal part. Next, the single-level discrete 2-D wavelet transform is used to decompose each noisy image into its low-frequency and high-frequency parts. Then, singular value decomposition (SVD) is used to obtain the SVs of the three high-frequency parts. Finally, the three denoised high-frequency parts are reconstructed by SVD from the SV difference, and the final denoised image is obtained using the inverse wavelet transform. Experiments show the effectiveness of this method compared with relevant existing methods.

scholarly journals Nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography

2018 ◽  
Vol 23 (09) ◽  
pp. 1 ◽  
Author(s):  
Irina N. Dolganova ◽  
Nikita V. Chernomyrdin ◽  
Polina V. Aleksandrova ◽  
Sheykh-Islyam T. Beshplav ◽  
Alexander A. Potapov ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Optical Coherence ◽  
Wavelet Domain ◽  
Denoising Method

Bayesian Image Denoising Using an Anisotropic Markov Random Field Model

2011 ◽  
Vol 467-469 ◽  
pp. 2018-2023
Author(s):  
Yan Qiu Cui ◽  
Tao Zhang ◽  
Shuang Xu ◽  
Hou Jie Li
Keyword(s):  
Computational Complexity ◽  
Random Field ◽  
Image Denoising ◽  
Markov Random Field ◽  
Field Model ◽  
Wavelet Domain ◽  
Map Estimation ◽  
Denoising Method ◽  
Markov Random ◽  
Restoration Method

This paper presents a Bayesian denoising method based on an anisotropic Markov Random Field (MRF) model in wavelet domain in order to improve the image denoising performance and reduce the computational complexity. The classical single-resolution image restoration method using MRFs and the maximum a posteriori (MAP) estimation is extended to the wavelet domain. To obtain the accurate MAP estimation, a novel anisotropic MRF model is proposed under this framework. As compared to the simple isotropic MRF model, this new model can capture the intrascale dependencies of wavelet coefficients significantly better. Simulation results demonstrate our proposed method has a good denoising performance while reducing the computational complexity.

scholarly journals X-ray image denoising based on wavelet transform and median filter

2020 ◽  
Vol 5 (2) ◽  
pp. 435-442
Author(s):  
Hanlei Dong ◽  
Liguo Zhao ◽  
Yunxing Shu ◽  
Neal N. Xiong
Keyword(s):  
Wavelet Transform ◽  
Wavelet Coefficient ◽  
Median Filter ◽  
Coefficient Matrix ◽  
Wavelet Domain ◽  
Denoising Method ◽  
X Ray ◽  
Standard Pulse ◽  
Soft Threshold ◽  
The Difference

AbstractThis paper mainly proposed and researched based on wavelet transform, and then used the X-map denoising technique of value filter. In other words, the value image was filtered in the spatial domain, and the value filtering was used as the standard pulse (salt) noise, also used as in the wavelet domain. After the filtered image was decomposed by biorthogonal double wavelet transform, a wavelet coefficient matrix was generated, and a soft threshold quantisation process was performed on the wavelet coefficients to produce a new wavelet coefficient matrix. In the end, they used a new wavelet coefficient matrix for image reconstruction. The processing resulted that the denoising method proposed in this paper showed that the X image can be denoised, which not only reduced the X-picture-like noise but also preserved the X-picture-like details as much as possible. It also helped to enhance diagnostic accuracy and reduced the difference in reading.

Information Detection and Data Denoising Method Using Nano Electrode Array Sensor

2021 ◽  
Vol 16 (2) ◽  
pp. 303-311
Author(s):  
Cheng Le
Keyword(s):  
Heavy Metal ◽  
Gold Nanoparticles ◽  
Data Processing ◽  
Equivalent Circuit ◽  
Electrode Array ◽  
Electrode Polarization ◽  
Wavelet Domain ◽  
Denoising Method ◽  
Array Sensor ◽  
Metal Monitoring

Computer technology and sensor technology can be combined. The technology set can be used to monitor the concentration of heavy metals in soil, which can help to prevent the occurrence of heavy metal pollution in time. First, nanotechnology, electrode polarization and the advantages of gold nanoparticles modified electrode are studied, and the design method of the nano electrode array is further analyzed. Also, the internal parameters of the three-electrode equivalent circuit are studied, and the model of the three-electrode equivalent circuit is derived. On this basis, a heavy metal monitoring circuit based on the nano electrode array sensor is designed. While the information monitoring based on this circuit is performed, wavelet domain denoising technology is studied in data processing. In view of the defects of the general hard threshold in practical application, the threshold is improved to recognize the depth of denoising. In the experiment, gold nanoparticles modified mercury electrode is used as working electrode. According to the principle that the precipitation time is inversely proportional to the detection current, 0.01 mol/L HCl is selected as the solution environment; moreover, it is set that pH=4 and the precipitation time is 4 min. The results show that for the same kind of ions, with the increase of the concentration of ions to be measured, the scanning potential range remains unchanged, while the peak current increases significantly. Metal ions can be effectively identified based on the potential corresponding to peak value. In the data processing of the detection circuit, the improved signal denoising method is compared with the default threshold wavelet domain denoising technology. The results show that the improved wavelet domain denoising method has less signal error, and the denoising effect of heavy metal detection is obvious.

scholarly journals Non-uniform threshold as an alternative to uniform threshold in denoising in wavelet domain

2010 ◽  
Vol 23 (1) ◽  
pp. 139-146
Author(s):  
Mitko Kostov ◽  
Cvetko Mitrovski ◽  
Momcilo Bogdanov
Keyword(s):  
Filter Banks ◽  
Wavelet Domain ◽  
Wavelet Shrinkage ◽  
Denoising Method ◽  
Noise Energy ◽  
Noisy Signals ◽  
Wavelet Applications

In this paper we present the advantage of non-uniform over uniform threshold wavelet shrinkage denoising method, applied on noisy signals with signal dependent noise. We illustrate our results by comparing the noise energy after using the both filtration methods on the same set of artificially noise contaminated images. The experiments are made with NPR-QMF filter banks instead with the filter banks that are commonly used in wavelet applications.

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