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1

Improving Quality of Watermarked Medical Images Using Symmetric Dilated Convolution Neural Networks

Pulgam Namita D., Shinde Subhash K.

Journal of Telecommunications and Information Technology

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2023

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nr 2

46--52

EN

Rapid development of online medical technologies raises questions about the security of the patient’s medical data.When patient records are encrypted and labeled with a watermark, they may be exchanged securely online. In order to avoid geometrical attacks aiming to steal the information, image quality must be maintained and patient data must be appropriately extracted from the encoded image. To ensure that watermarked images are more resistant to attacks (e.g. additive noise or geometric attacks), diﬀerent watermarking methods have been invented in the past. Additive noise causes visual distortion and render the potentially harmful diseases more diﬃcult to diagnose and analyze. Consequently, denoising is an important pre-processing method for obtaining superior outcomes in terms of clarity and noise reduction and allows to improve the quality of damaged medical images. Therefore, various publications have been studied to understand the denoising methods used to improve image quality. The ﬁndings indicate that deep learning and neural networks have recently contributed considerably to the advancement of image processing techniques. Consequently, a system has been created that makes use of machine learning to enhance the quality of damaged images and to facilitate the process of identifying speciﬁc diseases. Images, damaged in the course of an assault, are denoised using the suggested technique relying on a symmetric dilated convolution neural network. This improves the system’s resilience and establishes a secure environment for the exchange of data while maintaining secrecy.

2

Grey wolf optimization-based variational mode decomposition for magnetotelluric data combined with detrended fluctuation analysis

Zhang Xian, Li Diquan, Li Jin, Li Yong

Acta Geophysica

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2022

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Vol. 70, no 1

111--120

EN

We present a magnetotelluric data denoising method that uses grey wolf optimization to optimize variational mode decomposition and combines it with detrended fluctuation analysis. First, envelope entropy is selected as the fitness function for grey wolf optimization and is used to determine the number of modes K and the penalty factor, which are the key parameters of the variational mode decomposition method. Then, the optimized variational mode decomposition method is used to decompose magnetotelluric data. Finally, the scaling exponent in detrended fluctuation analysis is used to determine the corresponding intrinsic mode function components to superimpose and reconstruct the useful magnetotelluric data. Extensive experiments and thorough analyses are performed on the synthetic data and field data. The results of the proposed method are compared with the results of the remote reference, variational mode decomposition, variational mode decomposition and matching pursuit, variational mode decomposition and detrended fluctuation analysis methods; the proposed method can improve the denoising performance and reliability of low-frequency magnetotelluric data. The reconstructed data are closer to the natural magnetotelluric data. The satisfactory performance in the results verifies the effectiveness of the design and optimization method.

3

A diagnostic algorithm diagnosing the failure of railway signal equipment

Wu Yongcheng, Cao Dejin

Diagnostyka

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2021

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Vol. 22, No. 4

33--38

EN

Failure of railway signal equipment can cause an impact on its normal operation, and it is necessary to make a timely diagnosis of the failure. In this study, the data of a railway bureau from 2016 to 2020 were studied as an example. Firstly, denoising and feature extraction were performed on the data; then the Adaptive Comprehensive Oversampling (ADASYN) method was used to synthesize minority class samples; finally, three algorithms, back-propagation neural network (BPNN), support vector machine (SVM) and C4.5 algorithms, were used for failure diagnosis. It was found that the three algorithms performed poorly in diagnosing the original data but performed significantly better in diagnosing the synthesized samples, among which the BPNN algorithm had the best performance. The average precision, recall rate and F1 score of the BPNN algorithm were 0.94, 0.92 and 0.93, respectively. The results verify the effectiveness of the BPNN algorithm for failure diagnosis, and the algorithm can be further promoted and applied in practice.

4

Research on Yarn Diameter and Unevenness Based on an Adaptive Median Filter Denoising Algorithm

Wang Xiao, Hou Ru-Meng, Gao Xiao-Yan, Xin Bin-Jie

Fibres & Textiles in Eastern Europe

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2020

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Vol. 28, no. 1 (139)

36--41

EN

In this paper an adaptive median filtering denoising algorithm is proposed to measure yarn diameter and its unevenness. Images of nine different yarn samples were captured using one set of a self-developed yarn image acquisition system. Image separation of the background and yarn sections was conducted using a combination of adaptive median filtering, adaptive threshold segmentation and morphological processing. The noise-free yarn image was used for diameter detection of the subsequent yarn image and the discrimination of the yarn unevenness. Experimental results show that the testing data of yarn unevenness detection based on the adaptive median filter denoising algorithm is very consistent with the data using the traditional method. It is proved that the yarn detection method proposed, based on an adaptive median filter denoising algorithm, is feasible. It can be used to calculate yarn diameter accurately and measure yarn unevenness efficiently, so as to determine the quality of yarn appearance objectively.

PL

W artykule zaproponowano algorytm odszumiania z adaptacyjnym filtrem medianowym (AMF) do pomiaru średnicy przędzy i jej nierówności. Obrazy dziewięciu różnych próbek przędzy zostały przechwycone przy użyciu jednego zestawu samodzielnie opracowanego systemu akwizycji obrazów przędzy. Rozdzielenie obrazu tła i odcinków przędzy przeprowadzono przy użyciu kombinacji AMF, adaptacyjnej segmentacji progowej i przetwarzania morfologicznego. Bezszumowy obraz przędzy wykorzystano do wykrywania średnicy przędzy i rozróżnienia nierówności przędzy. Wyniki eksperymentalne pokazały, że dane testowe dotyczące wykrywania nierówności przędzy w oparciu o zaproponowany algorytm miały wysoką zgodność z danymi uzyskanymi przy użyciu tradycyjnej metody. Algorytmu tego można użyć do dokładnego obliczenia średnicy przędzy i skutecznego pomiaru nierówności przędzy, aby obiektywnie określić jakość wyglądu przędzy.

5

Objective Edge Similarity Metric for denoising applications in MR images

Shanmugam Ashok, Rukmani Devi S.

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

574--582

EN

Edge Similarity Metrics (ESMs) are necessary to objectively quantify the inadvertent blur at the edge pixels which occurs during denoising. They are helpful for evaluating edge-preserving capability of nonlinear filters. Most of the ESMs in literature, consider similarity of either strength of the edges or their direction individually. They lag in terms of concordance with subjective edge similarity ratings. An Objective Edge Similarity Metric (OESM) which considers all three attributes of edges; strength, direction and width together, is proposed in this paper. Pearson's Correlation shown by Gradient Magnitude Similarity Deviation (GMSD), Gradient Similarity Measure (GSM), Edge Strength Similarity Index Metric (ESSIM) and OESM with Subjective Edge Similarity Score (SESS) are ˗0.9669 ± 0.0028, 0.9566 ± 0.0053, 0.9507 ± 0.0057 and 0.9848 ± 0.0038, respectively. OESM is able to measure the degree of edge similarity between images more efficiently than GMSD, GSM and ESSIM. It reflects the perceptual edge similarity between images more accurately than GMSD, GSM and ESSIM.

6

Reconstruction of dynamics of SO2 concentration in troposphere based

Gac Jakub M., Petelczyc Monika

Ecological Chemistry and Engineering. S

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2019

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Vol. 26, nr 1

59--68

EN

A method for the reconstruction of the dynamics of processes with discrete time, developed in our previous papers, has been applied for study the dynamics of concentration of sulfur dioxide in lower troposphere. For the analysis, recordings of sulfur dioxide concentration from four measurement stations located in Poland (two of them has been located in huge cities and two in rarely inhabited regions) were used. We managed to obtain the deterministic and stochastic component of this dynamics. In result, we estimate the lifetime of sulfur dioxide in troposphere and the increase of sulfur dioxide concentration influenced by anthropogenic sources.

7

A wavelet-based approach for business protocol discovery of web services from log files

Moudjari A., Kezzouli I., Talbi H., Draa A.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2019

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Vol. 67, nr 3

535--546

EN

Recently, business protocol discovery has taken more attention in the field of web services. This activity permits a better description of the web service by giving information about its dynamics. The latter is not supported by theWSDL language which concerns only the static part. The problem is that the only information available to construct the dynamic part is the set of log files saving the runtime interaction of the web service with its clients. In this paper, a new approach based on the Discrete Wavelet Transformation (DWT) is proposed to discover the business protocol of web services. The DWT allows reducing the problem space while preserving essential information. It also overcomes the problem of noise in the log files. The proposed approach has been validated using artificially-generated log files.

8

Super-resolution in Clinical Conditions : Deep Brain Stimulation Case Study

Feschet F., Lemaire J.-J.

Fundamenta Informaticae

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2018

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Vol. 163, nr 1

41--62

EN

Deep Brain Stimulation (DBS) has proven its efficiency in the treatment of Parkinson's disease or essential tremor. It requires precise localizations of targets for instance in the thalamus. Since deep brain structures have been shown to be hardly visible on T1 or T2 weighted imaging, most methods rely on atlas based comparison and registration. It is however possible to use direct targeting using a specific MRI sequence called WAIR (White Matter Attenuated Inversion Recovery) even on 1.5 Tesla MRI machine. The direct targeting facilitates the precise segmentation of deep brain structures needed to plan the trajectories of the electrodes for the DBS. But this remains a tedious delineation necessarily done by a neurosurgeon to avoid misinterpretation of the images. In this paper, we propose to build an isotropic super-resolution image for WAIR imaging to facilitate precise direct targeting of anatomical structures in the deep brain. We present a method to perform the reconstruction of a high resolution isotropic WAIR volume from three acquisitions performed on a volunteer subject. The method is based on transfinite interpolation in convex cells of an hyperplane arrangement. Our results show promising quality reconstruction for the computation of a super-resolution WAIR. It allows unambiguous segmentation of the deep brain to be used in DBS surgery.

9

An analytical method for the adaptive computation of threshold of gradient modulus in 2D anisotropic diffusion filter

Joseph J., Periyasamy R.

Biocybernetics and Biomedical Engineering

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2017

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Vol. 37, no. 1

1--10

EN

In spite of the extensive application of Anisotropic Diffusion (AD) filter in software packages for medical image analysis, denoising and edge preservation offered by it depends exclusively on the selection of the value of Threshold of Gradient Modulus (TGM). Tuning the TGM to its optimum value through trial and error is subjective and tiring. An analytical model to compute the optimum value of TGM adaptively from the mean gradient of the image itself is proposed in this article. The qualitative examination of the gradient and true edge maps of the original and restored Magnetic Resonance images revealed that analytically computed TGM ensures best trade-off between noise suppression and edge preservation.

10

Microseismic event denoising via adaptive directional vector median filters

Zheng J., Lu J.-R., Jiang T.-Q., Liang Z.

Acta Geophysica

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2017

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Vol. 65, no. 1

47--54

EN

We present a novel denoising scheme via Radon transform-based adaptive vector directional median filters named adaptive directional vector median filter (AD-VMF) to suppress noise for microseismic downhole dataset. AD-VMF contains three major steps for microseismic downhole data processing: (i) applying Radon transform on the microseismic data to obtain the parameters of the waves, (ii) performing S-transform to determine the parameters for filters, and (iii) applying the parameters for vector median filter (VMF) to denoise the data. The steps (i) and (ii) can realize the automatic direction detection. The proposed algorithm is tested with synthetic and field datasets that were recorded with a vertical array of receivers. The P-wave and S-wave direct arrivals are properly denoised for poor signal-to-noise ratio (SNR) records. In the simulation case, we also evaluate the performance with mean square error (MSE) in terms of signal-to-noise ratio (SNR). The result shows that the distortion of the proposed method is very low; the SNR is even less than dB.

11

A seismic interpolation and denoising method with curvelet transform matching filter

Yang H., Long Y., Lin J., Zhang F., Chen Z.

Acta Geophysica

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2017

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Vol. 65, no. 5

1029--1042

EN

A new seismic interpolation and denoising method with a curvelet transform matching filter, employing the fast iterative shrinkage thresholding algorithm (FISTA), is proposed. The approach treats the matching filter, seismic interpolation, and denoising all as the same inverse problem using an inversion iteration algorithm. The curvelet transform has a high sparseness and is useful for separating signal from noise, meaning that it can accurately solve the matching problem using FISTA. When applying the new method to a synthetic noisy data sets and a data sets with missing traces, the optimum matching result is obtained, noise is greatly suppressed, missing seismic data are filled by interpolation, and the waveform is highly consistent. We then verified the method by applying it to real data, yielding satisfactory results. The results show that the method can reconstruct missing traces in the case of low SNR (signal-to-noise ratio). The above three problems can be simultaneously solved via FISTA algorithm, and it will not only increase the processing efficiency but also improve SNR of the seismic data.

12

Adaptive wavelet thresholding for image denoising using sure minimization and clustering of wavelet coefficients

Fedak V., Nakonechny A.

Czasopismo Techniczne. Elektrotechnika

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2015

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Y. 112, iss. 2-E

197--210

EN

Images and video are often coded using block-based discrete cosine transform (DCT) or discrete wavelet transform (DWT) which cause a great deal of visual distortions. In this paper, an extension of the intra-scale dependencies of wavelet coefficients is proposed to improve denoising performance. This method incorporates information on neighbouring wavelet coefficients that are inside of manually created clusters. Extensive experimental results are given to demonstrate the strength of the proposed method.

PL

Obrazy i nagrania wideo są często kodowane z użyciem blokowej dyskretnej transformacji kosinusowej (DCT) lub dyskretnej transformacji falkowej (DWT), które powodują znaczne zakłócenia wizualne. W niniejszej pracy proponuje się rozszerzenie zależności między współczynnikami falkowymi dotyczącymi skali w celu zmniejszenia zaszumienia sygnału zakodowanego. Zaproponowana metoda zakłada wykorzystanie informacji o sąsiadujących współczynnikach falkowych, które znajdują się wewnątrz manualnie utworzonego klastra. W artykule zaprezentowano obszerne wyniki doświadczalne w celu wykazania jakości proponowanej metody.

13

Method for Identifying Micro-seismic P-Arrival by Time-frequency Analysis Using Intrinsic Time-Scale Decomposition

Zhang R., Zhang L.

Acta Geophysica

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2015

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Vol. 63, no. 2

468--485

EN

A method to identify the P-arrival of microseismic signals is proposed in this work, based on the algorithm of intrinsic timescale decomposition (ITD). Using the results of ITD decomposition of observed data, information of instantaneous amplitude and frequency can be determined. The improved ratio function of short-time average over long-time average and the information of instantaneous frequency are applied to the time-frequency-energy denoised signal for picking the P-arrival of the microseismic signal. We compared the proposed method with the wavelet transform method based on the denoised signal resulting from the best basis wavelet packet transform and the single-scale reconstruction of the wavelet transform. The comparison results showed that the new method is more effective and reliable for identifying P-arrivals of microseismic signals.

14

Wavelet-based forecasting of ARIMA time series - an empirical comparison of different methods

Schluter S., Deuschle C.

Managerial Economics

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2014

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Vol. 15, no. 1

107--131

EN

By means of wavelet transform, an ARIMA time series can be split into different frequency components. In doing so, one is able to identify relevant patters within this time series, and there are different ways to utilize this feature to improve existing time series forecasting methods. However, despite a considerable amount of literature on the topic, there is hardly any work that compares the different wavelet-based methods with each other. In this paper, we try to close this gap. We test various wavelet-based methods on four data sets, each with its own characteristies. Eventually we come to the conclusion that using wavelets does improve forecasting quality especially for time horizons longer than one-day-ahead. However, there is no single superior method: either wavelet-based denoising or wavelet-based time series decomposition is best. Performance depends on the data set as well as the forecasting time horizon.

15

How Random is Your Tomographic Noise? A Number Theoretic Transform (NTT) Approach

Fiorini R. A.

Fundamenta Informaticae

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2014

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Vol. 135, nr 1/2

135--170

EN

Discrete Tomography (DT), differently from GT and CT, focuses on the case where only few specimen projections are known and the images contain a small number of different colours (e.g. black-and-white). A concise review on main contemporary physical and mathematical CT system problems is offered. Stochastic vs. Combinatorially Optimized Noise generation is compared and presented by two visual examples to emphasise a major double-bind problem at the core of contemporary most advanced instrumentation systems. Automatic tailoring denoising procedures to real dynamic system characteristics and performance can get closer to ideal self-registering and selflinearizing system to generate virtual uniform and robust probing field during its whole designed service life-cycle. The first attempt to develop basic principles for system background low-level noise source automatic characterization, profiling and identification by CICT, from discrete system parameter, is presented. As a matter of fact, CICT can supply us with cyclic numeric sequences perfectly tuned to their low-level multiplicative source generators, related to experimental high-level overall perturbation (according to high-level classic perturbation computational model under either additive or multiplicative perturbation hypothesis). Numeric examples are presented. Furthermore, a practical NTT example is given. Specifically, advanced CT system, HRO and Mission Critical Project (MCP) for very low Technological Risk (TR) and Crisis Management (CM) system will be highly benefitted mostly by CICT infocentric worldview. The presented framework, concepts and techniques can be used to boost the development of next generation algorithms and advanced applications quite conveniently.

16

Porównanie metod redukcji szumu dla środowiskowych obrazów termograficznych

Bukowska-Belniak B., Dwornik M., Leśniak A.

Pomiary Automatyka Kontrola

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2013

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R. 59, nr 9

994--997

PL

W artykule przedstawione zostały standardowe oraz nowoczesne metody redukcji szumu dla termograficznych obrazów cyfrowych. Pokazano działanie kilku rodzajów filtracji różniących się zasadą działania: począwszy od metod działających w domenie przestrzeni lub częstotliwości do metod przestrzenno-częstotliwościowych (transformata falkowa, krzywkowa). Metody odszumiania przetestowane zostały zarówno na przykładzie temperaturowych obrazów syntetycznych, jak i na rzeczywistych środowiskowych obrazach termicznych.

EN

Thermography, as a fast and remote method of temperature imaging, can be used in environmental process monitoring [1, 2]. The recorded thermal images are noisy and low contrast. In Section 2 of the paper standard and modern methods of noise reduction for digital images are presented. The effect of several different types of filtration (operations in space or frequency domain [5, 6, 7]) and spatial-frequency transforms (wavelet transform (Fg. 1) [8] and curvelet transform [9]) are shown in Section 3. Noise reduction methods were tested both on synthetic temperature data examples and environmental thermal images. In order to examine the noise level of a camera, after the camera software corrections, the experiment (Fig. 2) was conducted. Fig. 3 shows the results of synthetic image denoising. Tab. 1 lists the mean square error for all the presented methods. In Section 4 the results of all the noise reduction methods for environmental images are presented (Figs. 4, 5). The best results for synthetic images were obtained for the wavelet transform using Daubechies wavelet family. This method required adapting several parameters. For both environmental images the Butterworth filtering, the wavelet and curvelet methods gave the bests results.

17

Odszumianie danych rejestrowanych wielokanałowo z użyciem transformaty falkowej

Jedliński Ł.

Eksploatacja i Niezawodność

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2012

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Vol. 14, No. 2

145-149

PL

W celu uzyskania informacji o interesującym nas zjawisku lub obiekcie najczęściej rejestrowane są wybrane sygnały pomiarowe otrzymane za pośrednictwem czujników. Niestety uzyskane sygnały oprócz pożądanej informacji zawierają również zakłócenia, które są spowodowane m.in. właściwościami toru pomiarowego i procesami towarzyszącymi działaniu obiektu. W wielu przypadkach zachodzi potrzeba pomiaru takiej samej wielkości w różnych miejscach obiektu i/lub kierunkach. Potrzebne są zatem narzędzia do poprawy stosunku sygnału do szumu sygnałów rejestrowanych wielokanałowo. Transformata falkowa jest stosunkowo nową metodą przetwarzania danych, która znalazła zastosowanie w różnych dziedzinach takich jak technika i fizyka. W odniesieniu do sygnałów może być używana do odszumiania, kompresji, wykrywaniu trendu czy nieciągłości sygnału. W pracy tej transformata falkowa została użyta od odszumiania sygnałów drgań zarejestrowanych z dwóch trójosiowych czujników. Obiektem badań była przekładnia zębata stożkowa. Odszumianie sygnałów miało na celu poprawę skuteczności diagnozy uszkodzenia kół zębatych przekładni.

EN

In order to obtain information regarding given phenomenon or object, it is usually necessary to register selected measurement signals obtained using sensors. Unfortunately, obtained signals, apart form desired information, contain disturbances caused by, amongst many other, properties of the measurement channel and processes associated with object operation. In many cases it is necessary to measure the same value in different places and/or directions. Thus, there is a demand for a tool improving signal to noise ration of the multi-channel registered signals.Wavelet transform is a relatively new method of data processing used in different fields (e.g. technique and physics). In case of signals it can be used for denoising, compression, trend detection or discontinuity detection. In this work it was used to denoise vibration signals registered by two three-axis sensors. Object of investigation was the bevel toothed gear. Signals denoising was to improve efficiency of the diagnosis of transmission gears teeth damage.

18

Komputerowe wspomaganie analizy obrazów sonograficznych szumów podwodnych

Jerzman B., Kiciński W.

Biuletyn Wojskowej Akademii Technicznej

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2012

|

Vol. 61, nr 2

115-125

PL

Praca dotyczy sposobu poprawy jakości zobrazowania sygnałów pomiarowych obserwowanych w środowisku podwodnym. Istotą zaproponowanego sposobu przetwarzania sygnałów hydroakustycznych jest transformacja sygnałów pomiarowych za pomocą falki Malvara, zobrazowanie współczynników falkowych w postaci sonogramu oraz odszumianie obrazu sonograficznego z wykorzystaniem estymatorów jądrowych funkcji gęstości prawdopodobieństwa. Opracowany w środowisku MATLAB program, po wczytaniu sygnałów pomiarowych zapisanych w kodzie ascii, tworzy obraz sonograficzny stanu środowiska podwodnego, a następnie realizuje procedurę odszumiania, mającą na celu poprawę jego jakości. Działanie programu zweryfikowano na rzeczywistych krótkookresowych, szerokopasmowych sygnałach pomiarowych zarejestrowanych w środowisku podwodnym.

EN

The article deals with the problem of improving the quality of imaging the measurement signals observed in the underwater environment. The essence of the proposed method of hydroacoustic signal processing is: the transform using the Malvar wavelet, imaging of the wavelet coefficients as a sonogram and denoising the image using the kernel density estimate. The application written in MATLAB environment reads the signals from files saved in ascii format, builds the sonogram of the state of the underwater environment and proceeds with the image denoising. The research was conducted on the real transient and broadband measurement signals recorded under the water.

19

Dealing with charged-coupled device noise in thin-layer videodensitometry. Optimization of several image-denoising techniques

Komsta Ł.

Acta Chromatographica

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2009

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Vol. 21, no. 3

355--367

EN

Charged-coupled device (CCD) noise can be a serious problem during videoscanning, especially when scanning dark plates with weakly fluorescent spots. The proper denoising of videoscans inside mathematical environments is a critical part of any advanced chemometric processing. The paper reports comparison and optimization of representative videoscan denoising by different techniques. Several kind of filters (averaging, circular, Gaussian, Savitzky-Golay, median, Wiener, FIR) and wavelet shrinkage (twelve mother wavelets from the Daubechies, Symmlet, and Coiflet family, five decomposition levels, and soft/hard thresholding) were optimized against noise autocorrelation or mean-squared error to the reference image. The reference image was obtained by grabbing and averaging 256 CCD frames. The median filter is the winner of the competition; other filters except Gaussian and wavelet shrinkage at high decomposition level are also sufficient and good ways of videoscan denoising. The Gaussian filter and wavelet shrinkage at low decomposition level performed worst and could not be recommended.

20

Comparative analysis of the chromatographic fingerprints of twenty different sage ( Salvia L.) species

Daszykowski M., Sajewicz M., Rzepa J., Hajnos M., Staszek D, Wojtal Ł., Kowalska T., Waksmundzka-Hajnos M., Walczak B.

Acta Chromatographica

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2009

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Vol. 21, no. 4

513--530

EN

Comparative analysis of twenty different sage ( Salvia L.) species grown in Poland has been performed on the basis of two types of chromatographic fingerprints. For efficient preprocessing and comparison of these fingerprints, chemometric methods were used. The main emphasis was on preprocessing of herbal fingerprints and selecting a suitable preprocessing strategy for exploring differences among them. After successful preprocessing of the fingerprints, principal component analysis was used to reveal chemical differences among the samples. An outcome of the comparative analysis was to pinpoint specific regions of the fingerprints indicative of differences among the samples. In fingerprints of the volatile fraction from the sage ( Salvia L.) species, obtained from head-space gas chromatography coupled with mass spectrometry, important regions were identified and associated with the presence of camphene, limonene, and eucalyptol in these samples.