Receivers with improved performance in the presence of co-channel interference based on the wavelet transform

Deblurring in the presence of noise is a hard problem, especially in ultrasonic and CT images. In this paper, we propose a new method of image deblurring in presence of noise, using symmetric Daubechies complex wavelet transform. The proposed method is based on shrinkage of multilevel Daubechies complex wavelet coefficients, and is adaptive as it uses shrinkage function based on the variance of wavelet coefficients as well as the mean and the median of absolute wavelet coefficients at a particular level. The results obtained after the application of the proposed method demonstrate an improved performance over other related methods available in literature.

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Noise2Noise Improved by Trainable Wavelet Coefficients for PET Denoising

Electronics ◽

10.3390/electronics10131529 ◽

2021 ◽

Vol 10 (13) ◽

pp. 1529

Author(s):

Seung-Kwan Kang ◽

Si-Young Yie ◽

Jae-Sung Lee

Keyword(s):

Wavelet Transform ◽

Wavelet Transforms ◽

High Dose ◽

Neural Network Learning ◽

Learning Framework ◽

Network Training ◽

Positron Emission ◽

Network Output ◽

Improved Performance ◽

Image Pairs

The significant statistical noise and limited spatial resolution of positron emission tomography (PET) data in sinogram space results in the degradation of the quality and accuracy of reconstructed images. Although high-dose radiotracers and long acquisition times improve the PET image quality, the patients’ radiation exposure increases and the patient is more likely to move during the PET scan. Recently, various data-driven techniques based on supervised deep neural network learning have made remarkable progress in reducing noise in images. However, these conventional techniques require clean target images that are of limited availability for PET denoising. Therefore, in this study, we utilized the Noise2Noise framework, which requires only noisy image pairs for network training, to reduce the noise in the PET images. A trainable wavelet transform was proposed to improve the performance of the network. The proposed network was fed wavelet-decomposed images consisting of low- and high-pass components. The inverse wavelet transforms of the network output produced denoised images. The proposed Noise2Noise filter with wavelet transforms outperforms the original Noise2Noise method in the suppression of artefacts and preservation of abnormal uptakes. The quantitative analysis of the simulated PET uptake confirms the improved performance of the proposed method compared with the original Noise2Noise technique. In the clinical data, 10 s images filtered with Noise2Noise are virtually equivalent to 300 s images filtered with a 6 mm Gaussian filter. The incorporation of wavelet transforms in Noise2Noise network training results in the improvement of the image contrast. In conclusion, the performance of Noise2Noise filtering for PET images was improved by incorporating the trainable wavelet transform in the self-supervised deep learning framework.

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Linearized Kalman Filter aided Wavelet Transform with Adaptive Thresholding Methodfor ECG signal

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k1317.10812s19 ◽

2019 ◽

Vol 8 (12S) ◽

pp. 1153-1164

Keyword(s):

Kalman Filter ◽

Wavelet Transform ◽

Signal Analysis ◽

Disease Classification ◽

Adaptive Thresholding ◽

Signal Acquisition ◽

Ecg Signal ◽

Threshold Method ◽

Novel Approach ◽

Improved Performance

Cardiovascular diseases (CVD) are the most chronic and dangerous diseases in worldwide. The early prediction of CVD can help to prevent deaths due to these diseases, using bio-medical signal analysis. In this field, the ECG signal plays an important role due to its significant nature of providing the health-related information. However, the signal acquisition process is a crucial step where signals get corrupted due to electrode movement, muscle movement and other types of interference which can degrade the performance of the signal analysis. Several approaches have been introduced but achieving the desired performance robustly is still considered as a challenging task. This paper presents a novel approach for ECG signal filtering by combining a combination of the extended Kalman filter, wavelet transform and an adaptive thresholding approach called as Linearized Kalman Filter aided Wavelet transform with Adaptive Thresholding (LKFWAT). In this process, the initial states of the signal are observed using a Kalman filter, later; a linearization scheme is presented to represent the signal in the linear form. Finally, an adaptive threshold method is applied to reduce the noise during signal construction. It will show the significant improvement in next level process of disease classification. A comparative experimental analysis is carried out which shows that the proposed approach achieves improved performance when compared with the state-of-art ECG denoising techniques.

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Toward The Simultaneous Correction of Spherical and Chromatic Aberration in Electron Optics by Means of an Electrostatic Electron Mirror

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100179786 ◽

1990 ◽

Vol 48 (1) ◽

pp. 206-207

Author(s):

Gertrude. F. Rempfer

Keyword(s):

Chromatic Aberration ◽

Transverse Magnetic ◽

Objective Lens ◽

Ion Imaging ◽

Corrected Image ◽

Electric And Magnetic Fields ◽

Chromatic Aberrations ◽

Improved Performance ◽

Electron Microscopes ◽

Image Planes

Optimum performance in electron and ion imaging instruments, such as electron microscopes and probe-forming instruments, in most cases depends on a compromise either between imaging errors due to spherical and chromatic aberrations and the diffraction error or between the imaging errors and the current in the image. These compromises result in the use of very small angular apertures. Reducing the spherical and chromatic aberration coefficients would permit the use of larger apertures with resulting improved performance, granted that other problems such as incorrect operation of the instrument or spurious disturbances do not interfere. One approach to correcting aberrations which has been investigated extensively is through the use of multipole electric and magnetic fields. Another approach involves the use of foil windows. However, a practical system for correcting spherical and chromatic aberration is not yet available.Our approach to correction of spherical and chromatic aberration makes use of an electrostatic electron mirror. Early studies of the properties of electron mirrors were done by Recknagel. More recently my colleagues and I have studied the properties of the hyperbolic electron mirror as a function of the ratio of accelerating voltage to mirror voltage. The spherical and chromatic aberration coefficients of the mirror are of opposite sign (overcorrected) from those of electron lenses (undercorrected). This important property invites one to find a way to incorporate a correcting mirror in an electron microscope. Unfortunately, the parts of the beam heading toward and away from the mirror must be separated. A transverse magnetic field can separate the beams, but in general the deflection aberrations degrade the image. The key to avoiding the detrimental effects of deflection aberrations is to have deflections take place at image planes. Our separating system is shown in Fig. 1. Deflections take place at the separating magnet and also at two additional magnetic deflectors. The uncorrected magnified image formed by the objective lens is focused in the first deflector, and relay lenses transfer the image to the separating magnet. The interface lens and the hyperbolic mirror acting in zoom fashion return the corrected image to the separating magnet, and the second set of relay lenses transfers the image to the final deflector, where the beam is deflected onto the projection axis.

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