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Generally Gaussian noise model is used in image processing applications due to its presence in electronic circuitry used in image acquisition devices like Charge Coupled Device (CCD) cameras. Off let due to discrete nature of electromagnetic radiation in image acquisition process, due to photon noise that follows poison distribution is also exist with Gaussian noise. In this paper we present an algorithm...
To improve the performance of denoising algorithm for industrial radiographic testing (RT) images, an optimized wavelet denoising algorithm using hybrid noise model (WDHM) is proposed. Firstly, a hybrid noise model is constructed by analyzing the noise components to solve the problem of noise variance estimation when wavelet denoising is adopted for RT images. Then, a wavelet processing threshold...
Wavelet Transform is a widely used tool in signal processing which helps in localizing a signal in both time and frequency domain. This is in contrast to FFT which can only resolve a signal in frequency domain. Additionally, Wavelet Transform has been widely employed by electrical machines researchers for fault diagnosis and prognosis especially as means to extract features for classification purposes...
Condition monitoring based on partial discharge diagnostics has seen a very rapid increase of interest from high voltage industry. Condition monitoring is an asset monitoring tool that can inform the user of real time health of their high voltage equipment / plant, which has now become a necessity in ageing networks that still have equipment in operation that has fulfilled its rated life. Partial...
Medical image processing is considered as an important topic in the domain of image processing. It is used to help the doctors to improve and speed up the diagnosis process. In particular, computed tomography scanners (CT-Scanner) are used to create cross-sectional medical 3D images of bones. In this paper, we propose a method for CT-Scanner identification based on the sensor noise analysis. We built...
Bearing health analysis plays a significant role in industry to improve reliability and performance of critical processes by alarming the faults at early stages. Conventional techniques do no guarantee to detect the faults at early stages because the low energy bearing frequencies get suppressed by stern noise and higher vibrations. The Fast Fourier Transform fails to analyse the transient and non-stationary...
Medical image degradation has a significant impact on image quality, and thus affects human interpretation and the accuracy of computer-assisted diagnostic techniques. Unfortunately, Ultrasound images are mainly degraded by an intrinsic noise called speckle. Therefore, despekle filtering is a critical preprocessing step in medical ultrasound images. In this paper we propose a new image denoising technique...
Heart sound has an important information that can help in diagnosis of the abnormality. This paper is developed based on the previous research to improve the feature in each types of abnormal heart sound. Wavelet decomposition is used for noise removal. Features are extracted by AR-PSD and used as inputs for classification. Finally 13 types of abnormal heart sound are classified into 13 categories...
Industrial robots are commonly used in production systems in order to improve productivity, quality and safety in manufacturing. There are many functions that can be carried out by industrial robots, and they represent the basic building blocks of the production sector. The ability to continuously monitor the status and condition of robots has become an important research issue in recent years and...
Side-channel power analysis attacks have been proven to be the most powerful attacks on implementations of cryptographic primitives. DPA and CPA are probably the most wide-spread practical attacks on numerous embedded cryptographic systems. Additive noise is a kind of typical power analysis resistant implementing technique. The success rate of the DPA and CPA attacks is significantly affected by the...
The Empirical Mode Decomposition (EMD) algorithm was introduced as the first step of the Hilbert-Huang Transform, proposed by Huang et al. (1998). EMD decomposes a signal into so-called Intrinsic Mode Functions (IMFs) in a systematic way. Since then, various versions of EMD have been developed, addressing weaknesses of the original EMD procedure and aiming to optimize the original algorithm in a number...
In this paper we propose the use of Support Vector Machine classifiers (SVM) and linear discriminant analysis (LDA) to determine the existence of magnetic flux leakage (MFL) in non-destructive testing (NDT for its acronym in English) performed on ferromagnetic sheets. These signals were provided by the Corporation for Research in Corrosion (CIC) and were acquired on a dyno. The signals are preprocessed...
Analysis of the fetal electrocardiogram (fECG) obtained from abdominal signals can be a difficult task as the signal of interest is a weak signal, buried in several other signal components. Automatic assessment of fetus condition using abdominal signals (ADS) can be an important tool for clinicians, but, due to the high noise and considering the variation of the fECG morphology, its implementation...
Content-based image retrieval (CBIR) technique retrieves relevant images based on extracted features from image contents. Latent semantic indexing (LSI) is used as a semantic model in the CBIR field. This paper investigates the capability of LSI-based CBIR in dealing with different types of image noise, and the impact of noise on the retrieval results. To construct the feature-image matrix (FIM) in...
As technology advances; blur in an image remains as an ever-present issue in the image processing field. A blurred image is mathematically expressed as a convolution of a blur function with a sharp image, plus noise. Removing blur from an image has been widely researched and is still important as new images are collected. Without a reference image, identifying, measuring, and removing blur from a...
In many medical imaging applications, a clear delineation and segmentation of areas of interest from low resolution images is crucial. It is one of the most difficult and challenging tasks in image processing and directiy determines the quality of final result of the image analysis. In preparation for segmentation, we first use preprocessing methods to remove noise and blur and then we use super-resolution...
According to the different characteristics between Infrared (IR) and visible image, an improved image fusion algorithm is proposed in this paper by using Nonsubsampled Contourlet (NSCT) transform, combined with local energy and fuzzy logic. Firstly, an S-function is used to adaptively enhance the contrast of the IR image. Secondly, the IR and visible images are decomposed into a series of low frequency...
Synthetic Aperture Radar (SAR) images despeckling technique has been developed for many years. Most methods cannot strike a good balance between smoothing speckle noise and preserving structure. In order to both smoothing speckle and preserving structure, we proposed a despeckling method using singular value thresholding and iterative regularization which inspires from spatially adaptive iterative...
Classic coherence analysis has been commonly used as a effective method for the analysis of stationary signals. To study the instantaneous coherence between non-stationary signals, we extended the concept of coherence to time-varying coherence using some time-frequency analysis methods. Wavelet-based coherence is one of the most widely used time-varying coherence methods, but few researchers have...
After analyzing the speckle model of SAR, a SAR image de-noising method based on Wavelet-Contourlet transform and principal component analysis is presented. Compared with Wavelet transform and Contourlet transform, Wavelet-Contourlet transform can express images more sparsely and obtain image structure better. Most of the existing methods for image de-noising rely on accurate estimation of noise variance...
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