This book serves as an introduction to the flourishing field of super-resolution imaging. It is a compiled volume, with different authors for each of its 14 chapters. While not having a strong outline or textbook format, the chapters group into several sections.

Super-Resolution Imaging

Non-Invasive foetal electrocardiography (NI-FECG) represents an alternative foetal monitoring technique to traditional Doppler ultrasound approaches, that is non-invasive and has the potential to provide additional clinical information. However, despite the significant advances in the field of adult ECG signal processing over the past decades, the analysis of NI-FECG remains challenging and largely unexplored. This is mainly due to the relatively low signal-to-noise ratio of the FECG compared to the maternal ECG, which overlaps in both time and frequency. This article is intended to be used by researchers as a practical guide to NI-FECG signal processing, in the context of the above issues. It reviews recent advances in NI-FECG research including: publicly available databases, NI-FECG extraction techniques for foetal heart rate evaluation and morphological analysis, NI-FECG simulators and the methodology and statistics for assessing the performance of the extraction algorithms. Reference to the most recent work is given, recent findings are highlighted in the form of intermediate summaries, references to open source code and publicly available databases are provided and promising directions for future research are motivated. In particular we emphasise the need and specifications for building a new open reference database of NI-FECG signals, and the need for new algorithms to be benchmarked on the same database, employing the same evaluation statistics. Finally we motivate the need for research in NI-FECG to address morphological analysis, since this represent one of the most promising avenues for this foetal monitoring modality.

https://iopscience.iop.org/article/10.1088/0967-3334/37/5/R1/pdf

A practical guide to non-invasive foetal electrocardiogram extraction and analysis.

A new improved Kurtogram was proposed in this paper Instead of Kurtosis, correlated Kurtosis of envelope signal extracted from the wavelet packet node was used as an indicator to determine the optimal frequency band Correlated Kurtosis helps to determine the fault related impulse signals not affected by other unrelated signal components Finally, two simulated and three experimental bearing fault cases are used to validate the effectiveness of proposed method and to compare with other similar methods The results demonstrate it can locate resonant frequency band with a high reliability than two previous developed methods by Lei et al and Wang et al especially for the incipient faults under low load

/pdf/a-new-improved-kurtogram-and-its-application-to-bearing-3o0mmpya2q.pdf

A New Improved Kurtogram and Its Application to Bearing Fault Diagnosis

Acoustic emission detection of fatigue cracks in wind turbine blades based on blind deconvolution separation

Objective. The fast fixed-point algorithm for independent component analysis (FastICA) has been widely used in fetal electrocardiogram (ECG) extraction. However, the FastICA algorithm is sensitive to the initial weight vector, which affects the convergence of the algorithm. In order to solve this problem, an improved FastICA method was proposed to extract fetal ECG. Methods. First, the maternal abdominal mixed signal was centralized and whitened, and the overrelaxation factor was incorporated into Newton’s iterative algorithm to process the initial weight vector randomly generated. The improved FastICA algorithm was used to separate the source components, selected the best maternal ECG from the separated source components, and detected the R-wave location of the maternal ECG. Finally, the maternal ECG component in each channel was removed by the singular value decomposition (SVD) method to obtain a clean fetal ECG signal. Results. An annotated clinical fetal ECG database was used to evaluate the improved algorithm and the conventional FastICA algorithm. The average number of iterations of the algorithm was reduced from 35 before the improvement to 13. Correspondingly, the average running time was reduced from 1.25 s to 1.04 s when using the improved algorithm. The signal-to-noise ratio (SNR) based on eigenvalues of the improved algorithm was 1.55, as compared to 0.99 of the conventional FastICA algorithm. The SNR based on cross-correlation coefficients of the conventional algorithm was also improved from 0.59 to 2.02. The sensitivity, positive predictive accuracy, and harmonic mean ( ) of the improved method were 99.37%, 99.00%, and 99.19%, respectively, while these metrics of the conventional FastICA method were 99.03%, 98.53%, and 98.78%, respectively. Conclusions. The proposed improved FastICA algorithm based on the overrelaxation factor, while maintaining the rate of convergence, relaxes the requirement of initial weight vector, avoids the unbalanced convergence, reduces the number of iterations, and improves the convergence performance.

/pdf/an-improved-fastica-method-for-fetal-ecg-extraction-5f1ddtrmud.pdf

An Improved FastICA Method for Fetal ECG Extraction.

In this paper, we present a new approach of multi-frame super-resolution (SR). The SR techniques strongly depend on the availability of accurate motion estimation. When the estimation of motion is not well established, as usually happens for non-parametric motion, annoying artifacts appear in the super-resolved image. Since SR problems suffer from the motion and blur estimations, new techniques are considered to improve the registration and restoration steps. The proposed method consists of a non-parametric image registration based on diffusion regularization and a nonlocal Laplace regularizer combined with a bilateral filter (BTV) in the reconstruction step to remove noise and motion outliers. The diffusion registration is employed to handle the small deformation between the unregistered images, while the combination of nonlocal Laplace and BTV is used to increase the robustness of the restoration step with respect to the blurring effect and to the noise. We also prove the existence of a solution to the well posed registration problem. Simulation results using different images show the effectiveness and robustness of our algorithm against noise and outliers compared to other existing methods.

A multi-frame super-resolution using diffusion registration and a nonlocal variational image restoration

Fast communication: New blind source separation method of independent/dependent sources

Background
The electrocardiogram (ECG) is a diagnostic tool that records the electrical activity of the heart, and depicts it as a series of graph-like tracings, or waves. Being able to interpret these details allows diagnosis of a wide range of heart problems. Fetal electrocardiogram (FECG) extraction has an important impact in medical diagnostics during the mother pregnancy period. Since the observed FECG signals are often mixed with the maternal ECG (MECG) and the noise induced by the movement of electrodes or by mother motion, the separation process of the ECG signal sources from the observed data becomes quite complicated. One of its complexity is when the ECG sources are dependent, thus, in this paper we introduce a new approach of blind source separation (BSS) in the noisy context for both independent and dependent ECG signal source. This approach consist in denoising the observed ECG signals using a bilateral total variation (BTV) filter; then minimizing the Kullbak-Leibler divergence between copula densities to separate the FECG signal from the MECG one.

/pdf/a-new-method-for-the-extraction-of-fetal-ecg-from-the-2oge64qwj3.pdf

A new method for the extraction of fetal ECG from the dependent abdominal signals using blind source separation and adaptive noise cancellation techniques.

Abdelghani Ghazdali

Papers

A multi-frame super-resolution using diffusion registration and a nonlocal variational image restoration

Fast communication: New blind source separation method of independent/dependent sources

A new method for the extraction of fetal ECG from the dependent abdominal signals using blind source separation and adaptive noise cancellation techniques.

Blind noisy mixture separation for independent/dependent sources through a regularized criterion on copulas

A multi-frame super-resolution based on new variational data fidelity term