Papers by Keyword: Denoising

Abstract: Abstract. Magnetic Resonance Imaging (MRI) is a medical tool that is used to form images of organs, soft tissues, bones and almost all internal body structures. The MRI image acquisition process takes a long time. One of the efforts to shorten the examination acquisition time is using the parallel imaging technique, namely SENSE. However, SENSE has a weakness, namely reducing the Signal Noise to Ratio (SNR). One of the denoising methods that can increase SNR is the Nonlocal mean filter (NLM). Post-image acquisition denoising becomes a cheaper and more effective alternative to use. The aim of this research is to measure the increase of SNR value in MRI SENSE images between before the denoising technique and after the denoising technique. This research is expected to produce a faster scanning time and maintain the quality of the MRI image. This experimental research was carried out by applying the SENSE parallel imaging technique to R-factors 2 and 4. The sequence used is T2WI TSE on axial slice phantom. The T2WI TSE SENSE phantom MRI image was corrected with the NLM denoising technique to produce a better quality image. Each variation is measured image information before and after the denoising technique. Image information is assessed quantitatively by measuring SNR. The results of the parametric test showed that there was an increase in the SNR value after the application of the denoising technique on the Phantom T2WI TSE SENSE MRI image at r-factor 2 and r-factor 4. The different test on the SNR assessment resulted in a p value < 0.001.

Abstract: Degradation of images and segmentation are the two most demanding fields for medical image processing, particularly when explicitly applied. The involvement of noise not only deteriorates the visual quality but also the precision of the segmentation which is vital to the medical diagnosis process of development. The complicated and monotonous main task is to manually denoise medical images such as CT, ultrasound and large numbers of clinical routine MRI images. The medical image must be denoised automatically. The proposed approach is associated with less complexity, this follows from the fact that, the design of system and time for optimization. Results show their efficacy for noise removal in medical ultrasound and MRI images .The final results of the proposed scheme in terms of noise reduction and structural preservation are excellent. However the proposed scheme is compared with existing methods and the performance of the proposed method in terms of visual quality, image quality index, peak SNR and PSNR is shown to be superior to existing methods.

Abstract: This study introduces a frame-rate up-conversion method that uses a temporal wavelet zerotree-based shrinkage algorithm over motion trajectory of a video obtained by optical flow. The method starts by optical flow estimation for predicting initial estimates of inserted frame pixels. Then, the predicted frame pixels are denoised using a specific wavelet-based algorithm, where each pixel location is examined independently through its own temporal motion path. The denoising was performed by shrinking zero-tree footprints to remove temporal oddities. The resulting video was observed to have more fluent temporal flow as compared to optical flow - only interpolation.

Abstract: This paper proposes a novel anytime fuzzy supervisory expert system for online signal processing. We demonstrate via simulations that this system is able to follow slowly varying signals and heal the signal in case of missing input data. In the presence of contaminating noise, the supervisory system performs the automatic wavelet shrinkage procedure selection, which ensures to pick the proper algorithm that is the most efficient in the given scenario. The necessary level of wavelet decomposition is determined online by the fuzzy supervisory expert. The system applies orthogonal wavelet functions in order to reduce significantly the processing time of reconstruction. The paper also shows how the online threshold estimator selection module ensures the highest denoising efficiency by selecting the most suitable algorithm.

Abstract: This paper aims to apply the wavelet transform to the study of driver’s heart rate in different roadside landscape patterns. In the methodology, we describe the procedure in detail that implementing wavelet transform to denoise heart rate signal. The result shows the algorithm presented with the best performance is suitable to process heart rate signal. In the case study, taking advantage of the superiority of wavelet transform in time-frequency domain, it is apparent that heart rate is in a state of fluctuation continuously. That confirms that sensitivity of heart rate measure the mental workload. We also observe that landscape transition enhance driver’s heart rate on a small scale, which makes a positive effect on driver and can be adopted as a countermeasure against the fatigue of driver in the further road landscape design.

Abstract: A local adaptive neighborhood model is proposed in this paper in order to deal with the mistake judgment in the existing scanning beam point cloud denoising algorithms. Such a model regards larger curvatures as the potential noises, can select angle thresholds of noise points and the median values of filtering windows adaptively, so as address the issues of mistake judgment and missing judgment of the point clouds denoising algorithms with different curvatures. The adaption theory in the angle threshold denoising algorithm classifies the noise points and data points. Therefore, it can ensure the smoothness in low frequency, and as well keep the high frequency characteristics. The new method improves the accuracy of median filtering, prevents the diffusion of noise, remove noises effectively while preserving sharp features, and avoid fuzzy data margin.

Abstract: In this paper, the author introduces the principles of wavelet packet denoising, conducts a simulated analysis on the improved performance of wavelet packet denoising and develops the source Matlab program. In addition, the measured acoustic signals of seafloor sediments are denoised using wavelet packet. It is feasible to apply the wavelet packet denoising technique in a wide range of engineering testing fields involving denoising operation.

Abstract: Considering lifting scheme and traditional wavelet packet transform principle, The optimal lifting wavelet packet threshold denoising algorithm was introduced. Experimental blasting vibration signal was decomposed by optimal lifting wavelet packet, and noise components in blasting vibration measured signals were filtered successfully. Research shows that, lifting wavelet package transform can effectively remove noise components, and it laid an important foundation for lifting algorithm will be introduced into the analysis field of blasting vibration effects and other mechanical vibration signal.

Abstract: In this paper we present a compound anisotropic diffusion filter algorithm to apply edge sensitive ICOV operator in NCD model. According to the correlation coefficient of the ICOV operator, we obtain effective nonlinear denoising. The experiment have validated that our algorithm have better effect in smoothing and better ability in edge preservation.

Abstract: Nowadays, signal attracts more and more concerns from all walks of life as an information carrier in the information age. However, Fourier Transform which separately deals with time and frequency domains may not be fully competent as a signal processer in the digitization of communications system. Wavelet analysis is a strong signal processing method, which can not only fetch the features of the signal, but also achieve the signal denoising, compression, determine trends, and other functions .Image denoising and voice denoising were two empirical analyses in this study, which indicate certain research value of wavelet analysis for the development of signal processing.