scholarly journals Sparse Representation Denoising for Radar High Resolution Range Profiling

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Min Li ◽  
Gongjian Zhou ◽  
Bin Zhao ◽  
Taifan Quan
Keyword(s):  
High Resolution ◽  
Sparse Representation ◽  
Noise Level ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Automatic Target Recognition ◽  
Time Shift ◽  
Denoising Method ◽  
Redundant Dictionary ◽  
Noise Level Estimation

Radar high resolution range profile has attracted considerable attention in radar automatic target recognition. In practice, radar return is usually contaminated by noise, which results in profile distortion and recognition performance degradation. To deal with this problem, in this paper, a novel denoising method based on sparse representation is proposed to remove the Gaussian white additive noise. The return is sparsely described in the Fourier redundant dictionary and the denoising problem is described as a sparse representation model. Noise level of the return, which is crucial to the denoising performance but often unknown, is estimated by performing subspace method on the sliding subsequence correlation matrix. Sliding window process enables noise level estimation using only one observation sequence, not only guaranteeing estimation efficiency but also avoiding the influence of profile time-shift sensitivity. Experimental results show that the proposed method can effectively improve the signal-to-noise ratio of the return, leading to a high-quality profile.

scholarly journals An Adaptive Boosting Algorithm for Image Denoising

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Zhuang Fang ◽  
Xuming Yi ◽  
Liming Tang
Keyword(s):  
Image Denoising ◽  
Noise Level ◽  
Signal To Noise Ratio ◽  
Visual Quality ◽  
Estimation Algorithm ◽  
Noisy Image ◽  
Adaptive Boosting ◽  
Noise Level Estimation ◽  
Parameter Configuration ◽  
Boosting Algorithm

Image denoising is an important problem in many fields of image processing. Boosting algorithm attracts extensive attention in recent years, which provides a general framework by strengthening the original noisy image. In such framework, many classical existing denoising algorithms can improve the denoising performance. However, the boosting step is fixed or nonadaptive; i.e., the noise level in iteration steps is set to be a constant. In this work, we propose a noise level estimation algorithm by combining the overestimation and underestimation results. Based on this, we further propose an adaptive boosting algorithm that excludes intricate parameter configuration. Moreover, we prove the convergence of the proposed algorithm. Experimental results that are obtained in this paper demonstrate the effectiveness of the proposed adaptive boosting algorithm. In addition, compared with the classical boosting algorithm, the proposed algorithm can get better performance in terms of visual quality and peak signal-to-noise ratio (PSNR).

scholarly journals Simultaneous denoising of multicomponent microseismic data by joint sparse representation with dictionary learning

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. KS155-KS172
Author(s):  
Jie Shao ◽  
Yibo Wang ◽  
Yi Yao ◽  
Shaojiang Wu ◽  
Qingfeng Xue ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Time Consistency ◽  
Denoising Method ◽  
Microseismic Data ◽  
Joint Sparse Representation ◽  
Consistency Constraint

Microseismic data usually have a low signal-to-noise ratio, necessitating the application of an effective denoising method. Most conventional denoising methods treat each component of multicomponent data separately, e.g., denoising methods with sparse representation. However, microseismic data are often acquired with a 3C receiver, especially in borehole monitoring cases. Independent denoising ignores the relative amplitudes and vector relationships between different components. We have developed a new simultaneous denoising method for 3C microseismic data based on joint sparse representation. The three components are represented by different dictionary atoms; the dictionary can be fixed or adaptive depending on the dictionary learning method that is used. Our method adds an extra time consistency constraint with simultaneous transformation of 3C data. The joint sparse optimization problem is solved using the extended orthogonal matching pursuit. Synthetic microseismic data with a double-couple source mechanism and two field downhole microseismic data were used for testing. Independent denoising of 1C data with the fixed dictionary method and simultaneous denoising of 3C data with the fixed dictionary and dictionary learning (3C-DL) methods were compared. The results indicate that among the three methods, the 3C-DL method is the most effective in suppressing random noise, preserving weak signals, and restoring polarization information; this is achieved by combining the time consistency constraint and dictionary learning.

Rapid Blind Denoising Method for Grating Fringe Images Based on Noise Level Estimation

2021 ◽  
pp. 1-1
Author(s):  
Shenhua Zhang ◽  
Yanxi Yang ◽  
Qiaomeng Qin ◽  
Lianqiang Feng ◽  
Licong Jiao
Keyword(s):  
Noise Level ◽  
Denoising Method ◽  
Noise Level Estimation

A novel segmentation based video-denoising method with noise level estimation

2014 ◽  
Vol 281 ◽  
pp. 507-520 ◽  
Author(s):  
Yang Cao ◽  
Shi Jie Zhang ◽  
Zheng Jun Zha ◽  
Jing Zhang ◽  
Chang Wen Chen
Keyword(s):  
Noise Level ◽  
Denoising Method ◽  
Noise Level Estimation ◽  
Video Denoising

Detection of a point defect in a silicon single crystal by high-resolution TEM

1995 ◽  
Vol 53 ◽  
pp. 466-467
Author(s):  
M. Awaji
Keyword(s):  
High Resolution ◽  
Single Crystal ◽  
Point Defect ◽  
Point Defects ◽  
Noise Level ◽  
Dark Field ◽  
Silicon Single Crystal ◽  
High Resolution Image ◽  
Dark Field Imaging ◽  
Field Imaging

It is necessary to improve the resolution, brightness and signal-to-noise ratio(s/n) for the detection and identification of point defects in crystals. In order to observe point defects, multi-beam dark-field imaging is one of the useful methods. Though this method can improve resolution and brightness compared with dark-field imaging by diffuse scattering, the problem of s/n still exists. In order to improve the exposure time due to the low intensity of the dark-field image and the low resolution, we discuss in this paper the bright-field high-resolution image and the corresponding subtracted image with reference to a changing noise level, and examine the possibility for in-situ observation, identification and detection of the movement of a point defect produced in the early stage of damage process by high energy electron bombardment.The high-resolution image contrast of a silicon single crystal in the [10] orientation containing a triple divacancy cluster is calculated using the Cowley-Moodie dynamical theory and for a changing gaussian noise level. This divacancy model was deduced from experimental results obtained by electron spin resonance. The calculation condition was for the lMeV Berkeley ARM operated at 800KeV.

Progress in high-resolution CRYO-SEM imaging of viral particles

1996 ◽  
Vol 54 ◽  
pp. 818-819
Author(s):  
Ya Chen ◽  
Geoffrey Letchworth ◽  
John White
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
High Resolution ◽  
Structural Information ◽  
Signal To Noise Ratio ◽  
Flexible Structures ◽  
Simian Virus ◽  
Topographic Features ◽  
Viral Particles ◽  
Scanning Electron

Low-temperature high-resolution scanning electron microscopy (cryo-HRSEM) has been successfully utilized to image biological macromolecular complexes at nanometer resolution. Recently, imaging of individual viral particles such as reovirus using cryo-HRSEM or simian virus (SIV) using HRSEM, HV-STEM and AFM have been reported. Although conventional electron microscopy (e.g., negative staining, replica, embedding and section), or cryo-TEM technique are widely used in studying of the architectures of viral particles, scanning electron microscopy presents two major advantages. First, secondary electron signal of SEM represents mostly surface topographic features. The topographic details of a biological assembly can be viewed directly and will not be obscured by signals from the opposite surface or from internal structures. Second, SEM may produce high contrast and signal-to-noise ratio images. As a result of this important feature, it is capable of visualizing not only individual virus particles, but also asymmetric or flexible structures. The 2-3 nm resolution obtained using high resolution cryo-SEM made it possible to provide useful surface structural information of macromolecule complexes within cells and tissues. In this study, cryo-HRSEM is utilized to visualize the distribution of glycoproteins of a herpesvirus.

Robustness Speaker Recognition Based on Feature Space in Clean and Noisy Condition

2019 ◽  
Vol 9 (4) ◽  
pp. 497-506 ◽  
Author(s):  
Khamis A. Al-Karawi
Keyword(s):  
Speech Processing ◽  
Speaker Recognition ◽  
System Performance ◽  
Speaker Verification ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Feature Space ◽  
Signal To Noise ◽  
Verification Systems ◽  
Noisy Condition

Background & Objective: Speaker Recognition (SR) techniques have been developed into a relatively mature status over the past few decades through development work. Existing methods typically use robust features extracted from clean speech signals, and therefore in idealized conditions can achieve very high recognition accuracy. For critical applications, such as security and forensics, robustness and reliability of the system are crucial. Methods: The background noise and reverberation as often occur in many real-world applications are known to compromise recognition performance. To improve the performance of speaker verification systems, an effective and robust technique is proposed to extract features for speech processing, capable of operating in the clean and noisy condition. Mel Frequency Cepstrum Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GFCC) are the mature techniques and the most common features, which are used for speaker recognition. MFCCs are calculated from the log energies in frequency bands distributed over a mel scale. While GFCC has been acquired from a bank of Gammatone filters, which was originally suggested to model human cochlear filtering. This paper investigates the performance of GFCC and the conventional MFCC feature in clean and noisy conditions. The effects of the Signal-to-Noise Ratio (SNR) and language mismatch on the system performance have been taken into account in this work. Conclusion: Experimental results have shown significant improvement in system performance in terms of reduced equal error rate and detection error trade-off. Performance in terms of recognition rates under various types of noise, various Signal-to-Noise Ratios (SNRs) was quantified via simulation. Results of the study are also presented and discussed.

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