Noise Reduction, Enhancement and Classification for Sonar Images

2021 ◽  
pp. 4439-4452
Author(s):  
Noor H. Resham ◽  
Heba Kh. Abbas ◽  
Haidar J. Mohamad ◽  
Anwar H. Al-Saleh
Keyword(s):  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Window Size ◽  
Speckle Noise ◽  
Signal To Noise ◽  
Coherent Noise ◽  
Distance Method ◽  
Sliding Windows ◽  
Noise Ratio ◽  
Four Blocks

    Ultrasound imaging has some problems with image properties output. These affects the specialist decision. Ultrasound noise type is the speckle noise which has a grainy pattern depending on the signal. There are two parts of this study. The first part is the enhancing of images with adaptive Weiner, Lee, Gamma and Frost filters with 3x3, 5x5, and 7x7 sliding windows. The evaluated process was achieved using signal to noise ratio (SNR), peak signal to noise ratio (PSNR), mean square error (MSE), and maximum difference (MD) criteria. The second part consists of simulating noise in a standard image (Lina image) by adding different percentage of speckle noise from 0.01 to 0.06. The supervised classification based minimum distance method is used to evaluate the results depending on selecting four blocks located at different places on the image. Speckle noise was added with different percentage from 0.01 to 0.06 to calculate the coherent noise within the image. The coherent noise was concluded from the slope of the standard deviation with the mean for each noise. The results showed that the additive noise increased with the slide window size, while multiplicative noise did not change with the sliding window nor with increasing noise ratio. Wiener filter has the best results in enhancing the noise.

ROBUST SVD FILTERING FOR LOW SIGNAL-TO-NOISE RATIO SEISMIC DATA

Geophysics ◽  
2021 ◽  
pp. 1-51
Author(s):  
Chao Wang ◽  
Yun Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Singular Values ◽  
New Method ◽  
Signal To Noise ◽  
Coherent Noise ◽  
Edge Preserving ◽  
Singular Vectors ◽  
Reduced Rank ◽  
Noise Ratio

Reduced-rank filtering is a common method for attenuating noise in seismic data. As conventional reduced-rank filtering distinguishes signals from noises only according to singular values, it performs poorly when the signal-to-noise ratio is very low, or when data contain high levels of isolate or coherent noise. Therefore, we developed a novel and robust reduced-rank filtering based on the singular value decomposition in the time-space domain. In this method, noise is recognized and attenuated according to the characteristics of both singular values and singular vectors. The left and right singular vectors corresponding to large singular values are selected firstly. Then, the right singular vectors are classified into different categories according to their curve characteristics, such as jump, pulse, and smooth. Each kind of right singular vector is related to a type of noise or seismic event, and is corrected by using a different filtering technology, such as mean filtering, edge-preserving smoothing or edge-preserving median filtering. The left singular vectors are also corrected by using the filtering methods based on frequency attributes like main-frequency and frequency bandwidth. To process seismic data containing a variety of events, local data are extracted along the local dip of event. The optimal local dip is identified according to the singular values and singular vectors of the data matrices that are extracted along different trial directions. This new filtering method has been applied to synthetic and field seismic data, and its performance is compared with that of several conventional filtering methods. The results indicate that the new method is more robust for data with a low signal-to-noise ratio, strong isolate noise, or coherent noise. The new method also overcomes the difficulties associated with selecting an optimal rank.

Application of frequency-dependent multichannel Wiener filters to detect events in 2D three-component seismometer arrays

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. V133-V141 ◽  
Author(s):  
J. Wang ◽  
F. Tilmann ◽  
R. S. White ◽  
P. Bordoni
Keyword(s):  
Least Squares Method ◽  
Signal To Noise Ratio ◽  
Noise Levels ◽  
Signal To Noise ◽  
Coherent Noise ◽  
Gas Field ◽  
Frequency Dependent ◽  
Wiener Filters ◽  
Noise Ratio

Hydraulic fracture-induced microseismic events in producing oil and gas fields are usually small, and noise levels are high at the surface as a result of the heavy equipment in use. Similarly, in nonhydrocarbon settings, arrays for detecting local earthquakes will benefit from reduced noise levels and the ability to detect smaller events will be increased. We propose a frequency-dependent multichannel Wiener filtering technique with linear constraints that uses an adaptive least-squares method to remove coherent noise in seismic array data. The noise records on several reference channels are used to predict the noise on a primary channel and then can be subtracted from the observed data. On a test with an unconstrained version of this filter, maximal noise suppression leads to signal distortion. Two methods of im-posing constraints then achieve signal preservation. In one case study, synthetic signals are added to noise from a pilot deployment of a hexagonal array (nine three-component seismometers, approximately [Formula: see text]) above a gas field; noise levels are suppressed by up to [Formula: see text] (at [Formula: see text]). In a second case study, natural seismicity recorded at a dense array ([Formula: see text] spacing) in Italy is used, where the application of the filter improves the signal-to-noise ratio (S/N) more than [Formula: see text] (at [Formula: see text]) using 35 stations. In both cases, the performance of the multichannel Wiener filters is significantly better than stacking, espe-cially at lower frequencies where stacking does not help to suppress the coherent noise. The unconstrained version of the filter yields the best improvement in signal-to-noise ratio, but the constrained filter is useful when waveform distortion is unacceptable.

scholarly journals Analysis and Implementation of Enhanced Image Inpainting method using adjustable patch sizes

2021 ◽  
Vol 9 (3) ◽  
pp. 310-317
Keyword(s):  
Mean Square Error ◽  
Patch Size ◽  
Signal To Noise Ratio ◽  
Image Inpainting ◽  
Absolute Difference ◽  
Mean Square ◽  
Patch Selection ◽  
Signal To Noise ◽  
Distance Method ◽  
Noise Ratio

Image inpainting is the process of reconstruction of the damaged image and removal of unwanted objects in an image. In the image inpainting process patch priority andselection of best patch playsa major role. The patch size is also considered for producing good results in the image inpainting. In this paper patch priority is obtained by introducing a regularization factor (ɷ). The best patch selection is acquired by using the Sum of Absolute Difference (SAD) distance method. The results of inpainting are investigated with adjustable patch sizes of 5×5, 7×7, 9×9, 11×11, and 13×13 for the proposed method. The performance of these adjustable patch sizes is observed by using Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). The best suitable patch size for good inpainting is announced based on the values of PSNR and MSE.

Elastographic Imaging Using Staggered Strain Estimates

2002 ◽  
Vol 24 (4) ◽  
pp. 229-245 ◽  
Author(s):  
S. Srinivasan ◽  
J. Ophir ◽  
S.K. Alam
Keyword(s):  
Signal To Noise Ratio ◽  
Window Size ◽  
Poor Quality ◽  
Small Distance ◽  
Estimation Technique ◽  
Signal To Noise ◽  
Estimation Techniques ◽  
Strain Estimation ◽  
Noise Ratio ◽  
Data Windows

Conventional techniques in elastography estimate strain as the gradient of the displacement estimates obtained through crosscorrelation of pre- and postcompression rf A-lines. In these techniques, the displacements are estimated over overlapping windows and the strains are estimated as the gradient of the displacement estimates over adjacent windows. The large amount of noise at high window overlaps may result in poor quality elastograms, thus restricting the applicability of conventional strain estimation techniques to low window overlaps, which, in turn, results in a small number of pixels in the image. To overcome this restriction, we propose a multistep strain estimation technique. It computes the first elastogram using nonoverlapped windows. In the next step, the data windows are shifted by a small distance (small fraction of window size) and another elastogram is produced. This is repeated until the cumulative shift equals/exceeds the window size and all the elastograms are staggered to produce the final elastogram. Simulations and experiments were performed using this technique to demonstrate significant improvement in the elastographic signal-to-noise ratio ( SNRe) and the contrast-to-noise ratio ( CNRe) at high window overlaps over conventional strain estimation techniques, without noticeable loss of spatial resolution. This technique might be suitable for reducing the algorithmic noise in the elastograms at high window overlaps.

scholarly journals Novel image enhancement approaches for despeckling in ultrasound images for fibroid detection in human uterus

2021 ◽  
Vol 11 (1) ◽  
pp. 399-410
Author(s):  
Kaitheri Thacharedath Dilna ◽  
Duraisamy Jude Hemanth
Keyword(s):  
Image Processing ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Signal To Noise ◽  
Statistical Measures ◽  
Noise Ratio ◽  
Scanned Images

Abstract Ultrasonography is an extensively used medical imaging technique for multiple reasons. It works on the basic theory of echoes from the tissues under consideration. However, the occurrence of signal dependent noise such as speckle destroys utility of ultrasound images. Speckle noise is subject to the composition of image tissue and parameters of image. It reduces the effectiveness of many image processing steps and decreases human perception of fine details form ultrasound images. In many medical image processing methods, despeckling is used as the preprocessing step before segmentation and feature extraction. Many speckle reduction filters are proposed but while combining many techniques some speckle diagnostic information should be preserved. Removal of speckle noise from ultrasound image by preserving edges and added features is a great challenging task in ultrasound image restoration. This paper aims at a comprehensive description and comparison of reduction of speckle noise of ultrasound fibroid image. Many filters are applied on ultrasound scanned images and the performance is marked in terms of some statistical measures. Even though several despeckling filters are there for speckle reduction, all are not good for ultrasound scanned images. A comparison of quality measures such as mean square error, peak signal-to-noise ratio, and signal-to-noise ratio is done in ultrasound images in despeckling.

Resolution performance of Wiener filters

Geophysics ◽  
1983 ◽  
Vol 48 (7) ◽  
pp. 887-899 ◽  
Author(s):  
S. H. Bickel ◽  
D. R. Martinez
Keyword(s):  
Time Constant ◽  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Resolving Power ◽  
General Definition ◽  
Resolution Time ◽  
Signal To Noise ◽  
Temporal Separation ◽  
Noise Ratio

To improve the resolution of seismic events, one often designs a Wiener inverse filter that optimally (in the least‐squares sense) transforms a measured source signature into a spike. When this filter is applied to seismic data, the bandwidth of any noise which is present increases along with the bandwidth of the signal. Thus the signal‐to‐noise ratio is degraded. To reduce signal ambiguity it is common practice to prewhiten the Wiener filter. Prewhitening the filter improves the output signal‐to‐ambient noise ratio, but at the same time it reduces resolution. The ability to resolve the temporal separation between events is determined by the resolution time constant which we define as the ratio of signal energy to peak signal power from the filter. For unfiltered wavelets the resolution time constant becomes the reciprocal of resolving power recently described by Widess (1982). For matched filter signals the resolution time constant can be regarded as the inverse of the frequency span of the signal. Although it is satisfying that the resolution time constant definition agrees with other measures of resolution, this more general definition has two major advantages. First, it incorporates the effect of filtering; second, it is easily generalized to incorporate the effects of noise by assuming that the filter is a Wiener filter. For a given amount of noise the Wiener filter is a generalization of the matched filter. Marine seismic wavelets demonstrate how reducing the noise level improves the resolution of a Wiener filter relative to a matched filter. For these wavelets a point of diminishing return is reached, such that, to realize a further small increase in resolution, a large increase in input signal‐to‐noise ratio is required to maintain interpretable information at the output.

A Robust Hybrid Filter Based on Evolutionary Intelligence and Fuzzy Evaluation

2018 ◽  
Vol 18 (04) ◽  
pp. 1850023 ◽  
Author(s):  
Hadi Salehi ◽  
Javad Vahidi ◽  
Homayun Motameni
Keyword(s):  
Genetic Algorithm ◽  
Image Quality Assessment ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Bilateral Filter ◽  
Fuzzy Evaluation ◽  
Signal To Noise ◽  
Denoising Method ◽  
Final Model ◽  
Noise Ratio

In this paper, a novel denoising method based on wavelet, extended adaptive Wiener filter and the bilateral filter is proposed for digital images. Production of mode is accomplished by the genetic algorithm. The proposed extended adaptive Wiener filter has been developed from the adaptive Wiener filter. First, the genetic algorithm suggest some hybrid models. The attributes of images, including peak signal to noise ratio, signal to noise ratio and image quality assessment are studied. Then, in order to evaluate the model, the values of attributes are sent to the Fuzzy deduction system. Simulations and evaluations mentioned in this paper are accomplished on some standard images such as Lena, boy, fruit, mandrill, Barbara, butterfly, and boat. Next, weaker models are omitted by studying of the various models. Establishment of new generations performs in a form that a generation emendation is carried out, and final model has a more optimum quality compared to each two filters in order to obviate the noise. At the end, the results of this system are studied so that a comprehensive model with the best performance is to be found. Experiments show that the proposed method has better performance than wavelet, bilateral, Butterworth, and some other filters.

scholarly journals Performance Analysis of Various Filters for De-Speckling of Thyroid Ultrasound Images

2019 ◽  
Vol 9 (2) ◽  
pp. 910-917
Keyword(s):  
Mean Square Error ◽  
Image Quality Assessment ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

Thyroid ultrasonography is the most common and extremely useful, safe, and cost effective way to image the thyroid gland and its pathology. However, an inherent characteristic of Ultrasound (US) imaging is the presence of multiplicative speckle noise. Speckle noise reduces the ability of an observer to distinguish fine details, make diagnosis more difficult. It limits the effective implementation of image analysis steps such as edge detection, segmentation and classification. The main objective of this study is to compare the performance of various spatial and frequency domain filters so as to identify efficient and optimum filter for de-speckling Thyroid US images. The performance of these filters is evaluated using the image quality assessment parameters Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Square Error (MSE) and Root Mean Square Error (RMSE) for different speckle variance. Experimental work revealed that kuan filter resulted in higher PSNR, SNR, SSIM and least MSE, RMSE values compared to other filters

scholarly journals Reduction of Impulsive Noise from Speech and Audio Signals by using Sd-Rom Algorithm

2021 ◽  
Vol 10 (1) ◽  
pp. 265-268
Author(s):  
G.Manmadha Rao* ◽  
Raidu Babu D.N ◽  
Krishna Kanth P.S.L ◽  
Vinay B. ◽  
Nikhil V.
Keyword(s):  
Impulse Noise ◽  
Impulsive Noise ◽  
Rank Order ◽  
Signal To Noise Ratio ◽  
Audio Signal ◽  
Window Size ◽  
Mean Value ◽  
Audio Signals ◽  
Signal To Noise ◽  
Noise Ratio

Removal of noise is the heart for speech and audio signal processing. Impulse noise is one of the most important noise which corrupts different parts in speech and audio signals. To remove this type of noise from speech and audio signals the technique proposed in this work is signal dependent rank order mean (SD-ROM) method in recursive version. This technique is used to replace the impulse noise samples based on the neighbouring samples. It detects the impulse noise samples based on the rank ordered differences with threshold values. This technique doesn’t change the features and tonal quality of signal. Rank ordered differences is used for detecting the impulse noise samples in speech and audio signals. Once the sample is detected as corrupted sample, that sample is replaced with rank ordered mean value and this rank ordered mean value depends on the sliding window size and neighbouring samples. This technique shows good results in terms of signal to noise ratio (SNR) and peak signal to noise ratio (PSNR) when compared with other techniques. It mainly used for removal of impulse noises from speech and audio signals.

Sign in / Sign up

Export Citation Format

Share Document