scholarly journals Accuracy improvement of phase estimation in electron holography using noise reduction methods

Microscopy ◽  
2020 ◽  
Vol 69 (2) ◽  
pp. 123-131 ◽  
Author(s):  
Yoshihiro Midoh ◽  
Koji Nakamae
Keyword(s):  
Noise Reduction ◽  
Reduction Method ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Phase Estimation ◽  
Electron Holography ◽  
Electron Dose ◽  
Evaluation Indexes ◽  
Reduction Methods ◽  
Fourier Transform Phase

Abstract We try to improve the limit of the phase estimation of the interference fringe at low electron dose levels in electron holography by a noise reduction method. In this paper, we focus on unsupervised approaches to apply it to electron beam-sensitive and unknown samples and describe an overview of denoising methods used widely in image processing, such as wiener filter, total variation denoising, nonlocal mean filters and wavelet thresholding. We compare the wavelet hidden Markov model (WHMM) denoising that we have studied so far with the other conventional noise reduction methods. We evaluate the denoise performance of each method using the peak signal-to-noise ratio between noise-free and the target holograms (noisy or denoised holograms) and the root mean-square error (RMSE) between the true phase of the fringe and the measured phase by the discrete Fourier transform phase estimator. We show the denoised holograms for simulation and experimental data by using each noise reduction method and then discuss evaluation indexes obtained from these denoised holograms. From experimental results, it can be seen that the WHMM denoising can reduce the RMSE of fringe phase to about 1/4.5 for noisy simulation holograms and it has stable and good performance for noise reduction of observed holograms with various image qualities.

Gaussian Noise Filter Using Variable Step Time Matrix of PCNN

2011 ◽  
Vol 48-49 ◽  
pp. 551-554 ◽  
Author(s):  
Yuan Yuan Cheng ◽  
Hai Yan Li ◽  
Qi Xiao ◽  
Yu Feng Zhang ◽  
Xin Ling Shi
Keyword(s):  
Gaussian Noise ◽  
Spatial Information ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Wiener Filter ◽  
Noise Filter ◽  
Variable Step ◽  
Mean Filter ◽  
Reduction Methods ◽  
Novel Method

A novel method was brought forward for the purpose of filtering Gaussian noise effectively by using variable step time matrix of the simplified pulse coupled neural network (PCNN). Firstly, the time matrix of PCNN, related to the grayscale and spatial information of an image, is calculated to identify the noise polluted pixels. Subsequently, a variable step, a long step for strong noise and a short step for weak noise, based on the time matrix is applied to modify the grayscale of noised pixels in a sliding window. And then wiener filter is used to the image to further filter the noise. Experiments show that the proposed filter can remove Gaussian noise effectively than other noise reduction methods such as median filter, mean filter, wiener filter etc, and the filtered image is smooth and the details and edges are sharp. Compared with existing PCNN based Gaussian noise filter, the proposed filter gets higher Peak Signal-to-Noise Ratio (PSNR) and better performance.

scholarly journals Optimized Hybrid Model for Gaussian Noise Reduction images

2021 ◽  
Vol 19 (1) ◽  
pp. 9-17
Author(s):  
Lubna Farhi ◽  
Agha Yasir ◽  
Farhan Ur Rehman ◽  
Baqar A. Zardari ◽  
Ramsha Shakeel
Keyword(s):  
Noise Reduction ◽  
Hybrid Model ◽  
Gaussian Noise ◽  
Impulsive Noise ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Structural Similarity ◽  
Mean Value ◽  
The Matrix ◽  
High Level

In this paper, image noise is removed by using a hybrid model of wiener and fuzzy filters. It is a challenging task to remove Gaussian noise (GN) from an image and to protect the image’s edges. The Fuzzy-Wiener filter (FWF) hybrid model is used for optimizing the image smoothness and efficiency at a high level of GN. The efficiency is measured by using Structural Similarity (SSIM), Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). The proposed algorithm substitutes a mean value of the matrix for a non-overlapping block and replaces the total pixel number with each direction. In the proposed model, overall results proved that the optimized hybrid model FWF has an enormous computational speed and impulsive noise reduction, which enables efficient filtering as compared to the existing techniques.

The noise reduction of medical radiography images using fractional moments

2021 ◽  
Vol 42 (6) ◽  
pp. 649-658
Author(s):  
Mahdieh Gholizadeh ◽  
Mohammad Hossein Gholizadeh ◽  
Hossein Ghayoumi Zadeh ◽  
Mostafa Danaeian
Keyword(s):  
Noise Reduction ◽  
Moment Method ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
Radiographic Images ◽  
Fractional Moments ◽  
Reduction Methods ◽  
The Moment ◽  
The One ◽  
Medical Radiography

Background: This paper presents a method to improve medical radiography images based on the use of statistical signal moments. Methods: In this paper, the image with noise is considered as a statistical signal, and the noise reduction is performed by using fractional moments. The fractional moment’s method, on the one hand, has a speed similar to the moment method, and, on the other hand, has not the limitations of the moment method, which sometimes achieves inaccurate results. The proposed method is ultimately examined on radiographic images (CT). Results: The information obtained from the fractional moments of the received signal is a criterion to estimate the noise parameters and the gray scales of the main image. One of the limitations of the proposed method is that the image should be sent several times, because in statistical discussions, we cannot make a decision with only one sample. The error of the proposed noise reduction method in terms of the number of times the original image was sent, is about 0.009, 0.0009, 0.0002, and 0.0001, for n = 3, n = 6, n = 9 and n = 14, respectively. Conclusion: The simulation results show that the proposed method is more effective than the most conventional noise reduction methods, both in the low signal to noise ratio and in terms of image quality, and is more powerful than the most notable noise removal methods in restoring the subtleties and image details.

scholarly journals Improvement of Fast Simplified Successive-Cancellation Decoder for Polar Codes

Information ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 254
Author(s):  
Chao Xing ◽  
Zhiliang Huang ◽  
Shengmei Zhao
Keyword(s):  
Reduction Method ◽  
Signal To Noise Ratio ◽  
Code Rate ◽  
Polar Codes ◽  
Latency Reduction ◽  
Low Snr ◽  
Successive Cancellation ◽  
Current Node ◽  
Reduction Methods ◽  
Check Condition

This paper presents a new latency reduction method for successive-cancellation (SC) decoding of polar codes that performs a frozen-bit checking on the rate-other (R-other) nodes of the Fast Simplified SC (Fast-SSC) pruning tree. The proposed method integrates the Fast-SSC algorithm and the Improved SSC method (frozen-bit checking of the R-other nodes). We apply a recognition-based method to search for as many constituent codes as possible in the decoding tree offline. During decoding, the current node can be decoded directly, if it is a special constituent code; otherwise, the frozen-bit check is executed. If the frozen-bit check condition is satisfied, the operation of the R-other node is the same as that of the rate-one node. In this paper, we prove that the frame error rate (FER) performance of the proposed algorithm is consistent with that of the original SC algorithm. Simulation results show that the proportion of R-other nodes that satisfy the frozen-bit check condition increases with the signal-to-noise-ratio (SNR). Importantly, our proposed method yields a significant reduction in latency compared to those given by existing latency reduction methods. The proposed method solves the problem of high latency for the Improved-SSC method at a high code rate and low SNR, simultaneously.

scholarly journals Hybrid FWF Model For Gaussian Noise Reduction From Images

10.31645/11 ◽  
2020 ◽  
Author(s):  
Lubna Farhi ◽  
◽  
Farhan Ur Rehman ◽  
Keyword(s):  
Noise Reduction ◽  
Hybrid Model ◽  
Gaussian Noise ◽  
Impulsive Noise ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Structural Similarity ◽  
Mean Value ◽  
The Matrix ◽  
High Level

In this paper, the image efficiency is improved by using hybrid model of wiener’s filter and fuzzy filter. It’s a challenging task to remove Gaussian noise (GN) from an image and to protect the picture edges. The Fuzzy - Wiener filter (FWF) hybrid model is used for optimizing the image smoothness and efficiency at a high level of GN. The efficiency is measured by using Structural Similarity (SSIM), Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). The proposed algorithm substitutes a mean value of the matrix for a non-overlapping block and replaces the total pixel number with each direction. In the proposed model, overall results presented that the optimized hybrid model FWF has an enormous computational speed and impulsive noise reduction, which enables efficient filtering as compared to the existing techniques

scholarly journals Noise Reduction Method of Underwater Acoustic Signals Based on CEEMDAN, Effort-To-Compress Complexity, Refined Composite Multiscale Dispersion Entropy and Wavelet Threshold Denoising

Entropy ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 11 ◽  
Author(s):  
Guohui Li ◽  
Qianru Guan ◽  
Hong Yang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Reduction Method ◽  
Signal To Noise Ratio ◽  
Acoustic Signals ◽  
Ensemble Empirical Mode Decomposition ◽  
Underwater Acoustic ◽  
Real Signal ◽  
Mode Decomposition ◽  
Soft Threshold

Owing to the problems that imperfect decomposition process of empirical mode decomposition (EMD) denoising algorithm and poor self-adaptability, it will be extremely difficult to reduce the noise of signal. In this paper, a noise reduction method of underwater acoustic signal denoising based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), effort-to-compress complexity (ETC), refined composite multiscale dispersion entropy (RCMDE) and wavelet threshold denoising is proposed. Firstly, the original signal is decomposed into several IMFs by CEEMDAN and noise IMFs can be identified according to the ETC of IMFs. Then, calculating the RCMDE of remaining IMFs, these IMFs are divided into three kinds of IMFs by RCMDE, namely noise-dominant IMFs, real signal-dominant IMFs, real IMFs. Finally, noise IMFs are removed, wavelet soft threshold denoising is applied to noise-dominant IMFs and real signal-dominant IMFs. The denoised signal can be obtained by combining the real IMFs with the denoised IMFs after wavelet soft threshold denoising. Chaotic signals with different signal-to-noise ratio (SNR) are used for denoising experiments by comparing with EMD_MSE_WSTD and EEMD_DE_WSTD, it shows that the proposed algorithm has higher SNR and smaller root mean square error (RMSE). In order to further verify the effectiveness of the proposed method, which is applied to noise reduction of real underwater acoustic signals. The results show that the denoised underwater acoustic signals not only eliminate noise interference also restore the topological structure of the chaotic attractors more clearly, which lays a foundation for the further processing of underwater acoustic signals.

scholarly journals Enhancement of Speech Intelligibility using Binary Mask Based on Channel Selection Criteria

2020 ◽  
Vol 8 (5) ◽  
pp. 5123-5131
Keyword(s):  
Noise Reduction ◽  
Speech Intelligibility ◽  
Selection Criteria ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Wiener Filter ◽  
Gain Function ◽  
Signal To Noise ◽  
Short Time ◽  
Noise Reduction Algorithm

Most of the existing noise reduction algorithms used in hearing aid applications apply a gain function in order to reduce the noise intervention. In the present paper, we study the effect of the two types of speech distortions introduced by the gain functions. If these distortions are properly controlled large gains in intelligibility can be obtained. The sentences were corrupted by various kinds of noises i.e. babble noise, car noise, helicopter noise and random noise and processed through a noise-reduction algorithm. Subjective tests were conducted with normal hearing listeners by presenting the processed speech with controlled distortions. The method proposed by Kim et al uses the wiener filter. Here in this paper, we have used the parametric wiener filter. The experimental results clearly indicated improvement in intelligibility at 0dB, -5dB, +5dB and 10dB input signal-to-noise (SNR) values in short-time objective intelligibility (STOI) and Segmental signal-to-noise ratio (SSNR) objective measures.

scholarly journals Orthogonality-Constrained CNMF-Based Noise Reduction with Reduced Degradation of Biological Sound

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7981
Author(s):  
Naoto Murakami ◽  
Shota Nakashima ◽  
Katsuma Fujimoto ◽  
Shoya Makihira ◽  
Seiji Nishifuji ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Reduction Method ◽  
Signal To Noise Ratio ◽  
Respiratory Sounds ◽  
Chronic Obstructive ◽  
Noisy Environments ◽  
Obstructive Pulmonary Disease ◽  
Orthogonality Constraints ◽  
Average Improvement ◽  
Slow Progression

The number of deaths due to cardiovascular and respiratory diseases is increasing annually. Cardiovascular diseases with high mortality rates, such as strokes, are frequently caused by atrial fibrillation without subjective symptoms. Chronic obstructive pulmonary disease is another condition in which early detection is difficult owing to the slow progression of the disease. Hence, a device that enables the early diagnosis of both diseases is necessary. In our previous study, a sensor for monitoring biological sounds such as vascular and respiratory sounds was developed and a noise reduction method based on semi-supervised convolutive non-negative matrix factorization (SCNMF) was proposed for the noisy environments of users. However, SCNMF attenuated part of the biological sound in addition to the noise. Therefore, this paper proposes a novel noise reduction method that achieves less distortion by imposing orthogonality constraints on the SCNMF. The effectiveness of the proposed method was verified experimentally using the biological sounds of 21 subjects. The experimental results showed an average improvement of 1.4 dB in the signal-to-noise ratio and 2.1 dB in the signal-to-distortion ratio over the conventional method. These results demonstrate the capability of the proposed approach to measure biological sounds even in noisy environments.

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