scholarly journals Compressed Sensing Speech Signal Enhancement Research

2017 ◽  
Vol 6 (1) ◽  
pp. 26
Author(s):  
Kuangfeng Ning ◽  
Guojun Qin
Keyword(s):  
Compressed Sensing ◽  
Compressive Sensing ◽  
Input Signal ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Sampling Theorem ◽  
Compressed Domain ◽  
Signal To Noise ◽  
Perceptual Evaluation

<span lang="EN-US">The proposed Compressive sensing method is a new alternative method</span><span lang="EN-US">, it is</span><span lang="EN-US"> used to eliminate noise from the input signal</span><span lang="EN-US">,</span><span lang="EN-US"> and the quality of the speech signal </span><span lang="EN-US">is </span><span lang="EN-US">enhance</span><span lang="EN-US">d</span><span lang="EN-US"> with fewer samples</span><span lang="EN-US">, thus it is</span><span lang="EN-US"> required for the reconstruction than needed in some of the methods like Nyquist sampling theorem. The basic idea is</span><span lang="EN-US"> that </span><span lang="EN-US">the speech signals are sparse in nature</span><span lang="EN-US">,</span><span lang="EN-US"> and most of the noise signals are non-sparse in nature, and Compressive </span><span lang="EN-US">S</span><span lang="EN-US">ensing</span><span lang="EN-US">(</span><span lang="EN-US">CS) eliminates the non-sparse components and </span><span lang="EN-US">it </span><span lang="EN-US">reconstructs only the sparse components of the input signal. Experimental results prove that the average segmental SNR (signal to noise ratio) and PESQ (perceptual evaluation of speech quality) scores are better in the compressed domain</span><span lang="EN-US">．</span>

Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

2019 ◽  
Vol 12 (2) ◽  
pp. 138-151
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Wavelet Transform ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Perceptual Quality ◽  
Lifting Wavelet Transform ◽  
Signal To Noise ◽  
Perceptual Evaluation ◽  
Lifting Wavelet ◽  
Noise Ratio

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

Speech Enhancement on Over-Complete Dictionary and Threshold Orthogonal Matching Pursuit

2014 ◽  
Vol 1044-1045 ◽  
pp. 1463-1468
Author(s):  
Xiao Cui ◽  
Wu Qing Zhang
Keyword(s):  
Discrete Cosine Transform ◽  
Speech Enhancement ◽  
Speech Signal ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Orthogonal Matching Pursuit ◽  
Cosine Transform ◽  
Perceptual Evaluation ◽  
Transform Coefficients

In order to suppress the noise, improve equipment's ability to further process information and improve the quality of voice, speech enhancement is often an important part of the speech signal preprocess. Contrastively analyze the characteristic that the clean speech signal coefficients in over-complete discrete cosine dictionary are much sparser than the traditional discrete cosine transform coefficients. Under noisy conditions, by setting the iterative threshold of orthogonal matching pursuit (OMP) algorithm, clean speech can be gotten, thus realize the speech enhancement. Simulation results of the signal waveform and spectrogram enhanced by the proposed algorithm are very similar to the original signal,comparative experiments also indicate that the signal to noise ratio (SNR) and the perceptual evaluation of speech quality (PESQ) score of the processed signal are superior to traditional discrete cosine transform (DCT).

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

Multilag Correlation Estimators for Polarimetric Radar Measurements in the Presence of Noise

2012 ◽  
Vol 29 (6) ◽  
pp. 772-795 ◽  
Author(s):  
Lei Lei ◽  
Guifu Zhang ◽  
Richard J. Doviak ◽  
Robert Palmer ◽  
Boon Leng Cheong ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Data Quality ◽  
Signal To Noise Ratio ◽  
Simulated Data ◽  
Radar Data ◽  
Polarimetric Radar ◽  
Signal To Noise ◽  
Spectral Moments ◽  
Radar Measurements

Abstract The quality of polarimetric radar data degrades as the signal-to-noise ratio (SNR) decreases. This substantially limits the usage of collected polarimetric radar data to high SNR regions. To improve data quality at low SNRs, multilag correlation estimators are introduced. The performance of the multilag estimators for spectral moments and polarimetric parameters is examined through a theoretical analysis and by the use of simulated data. The biases and standard deviations of the estimates are calculated and compared with those estimates obtained using the conventional method.

scholarly journals Encryption Based Image Watermarking Algorithm in 2DWT-DCT Domains

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5540
Author(s):  
Nayeem Hasan ◽  
Md Saiful Islam ◽  
Wenyu Chen ◽  
Muhammad Ashad Kabir ◽  
Saad Al-Ahmadi
Keyword(s):  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Similarity Index ◽  
Structural Similarity ◽  
Reconstructed Image ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Watermarking Scheme

This paper proposes an encryption-based image watermarking scheme using a combination of second-level discrete wavelet transform (2DWT) and discrete cosine transform (DCT) with an auto extraction feature. The 2DWT has been selected based on the analysis of the trade-off between imperceptibility of the watermark and embedding capacity at various levels of decomposition. DCT operation is applied to the selected area to gather the image coefficients into a single vector using a zig-zig operation. We have utilized the same random bit sequence as the watermark and seed for the embedding zone coefficient. The quality of the reconstructed image was measured according to bit correction rate, peak signal-to-noise ratio (PSNR), and similarity index. Experimental results demonstrated that the proposed scheme is highly robust under different types of image-processing attacks. Several image attacks, e.g., JPEG compression, filtering, noise addition, cropping, sharpening, and bit-plane removal, were examined on watermarked images, and the results of our proposed method outstripped existing methods, especially in terms of the bit correction ratio (100%), which is a measure of bit restoration. The results were also highly satisfactory in terms of the quality of the reconstructed image, which demonstrated high imperceptibility in terms of peak signal-to-noise ratio (PSNR ≥ 40 dB) and structural similarity (SSIM ≥ 0.9) under different image attacks.

scholarly journals Performance of compressed sensing for fluorine‐19 magnetic resonance imaging at low signal‐to‐noise ratio conditions

2019 ◽  
Vol 84 (2) ◽  
pp. 592-608
Author(s):  
Ludger Starke ◽  
Andreas Pohlmann ◽  
Christian Prinz ◽  
Thoralf Niendorf ◽  
Sonia Waiczies
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Compressed Sensing ◽  
Signal To Noise Ratio ◽  
Resonance Imaging ◽  
Signal To Noise ◽  
Noise Ratio

The Influence of Alteration of kVp and mAs towards the Image Quality of Acrylic Using CBCT

2019 ◽  
Vol 829 ◽  
pp. 252-257
Author(s):  
Azhari ◽  
Yohanes Hutasoit ◽  
Freddy Haryanto
Keyword(s):  
Image Quality ◽  
Signal To Noise Ratio ◽  
Region Of Interest ◽  
Signal To Noise ◽  
Bite Block ◽  
Significant Difference ◽  
Noise Ratio ◽  
Post Hoc ◽  
Radiograph Image

CBCT is a modernized technology in producing radiograph image on dentistry. The image quality excellence is very important for clinicians to interpret the image, so the result of diagnosis produced becoming more accurate, appropriate, thus minimizing the working time. This research was aimed to assess the image quality using the blank acrylic phantom polymethylmethacrylate (PMMA) (C­5H8O2)n in the density of 1.185 g/cm3 for evaluating the homogeneity and uniformity of the image produced. Acrylic phantom was supported with a tripod and laid down on the chin rest of the CBCT device, then the phantom was fixed, and the edge of the phantom was touched by the bite block. Furthermore, the exposure of the X-ray was executed toward the acrylic phantom with various kVp and mAs, from 80 until 90, with the range of 5 kV and the variation of mA was 3, 5, and 7 mA respectively. The time exposure was kept constant for 25 seconds. The samples were taken from CBCT acrylic images, then as much as 5 ROIs (Region of Interest) was chosen to be analyzed. The ROIs determination was analyzed by using the ImageJ® software for recognizing the influence of kVp and mAs towards the image uniformity, noise and SNR. The lowest kVp and mAs had the result of uniformity value, homogeneity and signal to noise ratio of 11.22; 40.35; and 5.96 respectively. Meanwhile, the highest kVp and mAs had uniformity value, homogeneity and signal to noise ratio of 16.96; 26.20; and 5.95 respectively. There were significant differences between the image uniformity and homogeneity on the lowest kVp and mAs compared to the highest kVp and mAs, as analyzed with the ANOVA statistics analysis continued with the t-student post-hoc test with α = 0.05. However, there was no significant difference in SNR as analyzed with the ANOVA statistic analysis. The usage of the higher kVp and mAs caused the improvement of the image homogeneity and uniformity compared to the lower kVp and mAs.

Registration of the rotation axis in X-ray tomography

2015 ◽  
Vol 22 (2) ◽  
pp. 452-457 ◽  
Author(s):  
Yimeng Yang ◽  
Feifei Yang ◽  
Ferdinand F. Hingerl ◽  
Xianghui Xiao ◽  
Yijin Liu ◽  
...  
Keyword(s):  
Rotation Axis ◽  
Signal To Noise Ratio ◽  
Geological Samples ◽  
Signal To Noise ◽  
High Demand ◽  
X Ray ◽  
Data Registration ◽  
Reconstruction Quality ◽  
Tomographic Data

There is high demand for efficient, robust and automated routines for tomographic data reduction, particularly for synchrotron data. Registration of the rotation axis in data processing is a critical step affecting the quality of the reconstruction and is not easily implemented with automation. Existing methods for calculating the center of rotation have been reviewed and an improved algorithm to register the rotation axis in tomographic data is presented. The performance of the proposed method is evaluated using synchrotron-based microtomography data on geological samples with and without artificial reduction of the signal-to-noise ratio. The proposed method improves the reconstruction quality by correcting both the tilting error and the translational offset of the rotation axis. The limitation of this promising method is also discussed.

Sign in / Sign up

Export Citation Format

Share Document