Shock filter coupled with a high-order PDE for additive noise removal and image quality enhancement

Thierry, Simo; Welba, Colince; Pascal, Ntsama Eloundou; Noura, Alexendre

doi:10.1016/j.array.2021.100105

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…Another paper, innovates an unused filter for image boosting. This filter relayed on the combination of a shock channel with a fourth-order dissemination equation [19]. The limited result was noticed while applying this algorithm with images of degraded documents.…”

Section: Image De-noisingmentioning

confidence: 99%

A Hybrid Filtering Technique of Digital Images in Multimedia Data Warehouses

Othman¹,

Saad²,

Eldin³

2023

IJACSA

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The similarity search approach used for image Data Warehouse (DW) can provide better insights into discovering the most similar images compared to the input query. Due to the later innovation improvement, the mixed media complexity is discernibly expanded and modern inquires about regions are opened depending on comparable mixed media substance recovery. Content-Based Image Retrieval (CBIR) algorithms are utilized for the retrieval of images related to the inquiry image from gigantic databases or DW. The queries that are used for DW are complex, take a lot of time to process and many give less accurate results. For these reasons, this paper needs to have an effective technique to improve the similarity search query process that reflects a more positive result. In this paper, show how to extract features from a set of images (color, shape, and texture features) by using CBIR algorithm with Color Edge Detection (CED) method. Once these features are extracted, the proposed method will minimize the distance between these features vectors and the query image one using a Genetic Algorithm (GA). This paper illustrates the extraction of endless strong and imperative features from the database of the images, therefore, the capacity of these features in storing within the frame of features vectors. Accordingly, an imaginative closeness assessment with a metaheuristic algorithm (Genetic Algorithm (GA) with Simulating Annealing (SA)) has been attained between the query image features and those having a place in the database image. This paper introduces a new algorithm CEDF (Color Edge Detection with Gaussian Blur Filter) that applies the Gaussian Blur Filter after using CED method for feature detection of the image. Experimental results show that CEDF method gives better result than the other already-known methods.

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Section: Image De-noisingmentioning

confidence: 99%

A Hybrid Filtering Technique of Digital Images in Multimedia Data Warehouses

Othman¹,

Saad²,

Eldin³

2023

IJACSA

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Parameter estimation and velocity signal extraction for one-dimensional wave equation with harmonic corrupted boundary observation

Huang

Jin

2023

IMA Journal of Mathematical Control and Information

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In this paper, we consider parameter estimation and velocity signal extraction from a disturbed boundary velocity signal for an unstable wave equation. Firstly, an adaptive observer is designed based on the boundary displacement and the corrupted boundary velocity. Then the design of the feedback law adopts the backstepping method of infinite dimensional system. Finally, as time approaches infinity, the estimated parameters converge to the unknown parameters, the initial value disturbance can be obtained, and the velocity signal can be asymptotically recovered. Meanwhile, the asymptotic stability of the closed-loop system can be proved by $C_{0}$-semigroup theory and Lyapunov method. Numerical simulation shows that the proposed scheme is reasonable.

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Deep Siamese Domain Adaptation Convolutional Neural Network Optimized with Honey Badger Algorithm for Fetal Arrhythmia Detection and Classification from ECG Signals

Joel,

Sethukarasi

2024

Int. J. Patt. Recogn. Artif. Intell.

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Deep learning classification is commonly used to assess biomedical data. Fetal heart rate (FHR) signal data monitor the health of both the mother and the fetus and prevent mobility and death in fetuses at risk of hypoxia. But, the data imbalance is a major problem and happens frequently. To overcome this problem, a Deep Siamese domain adaptation convolutional Neural Network optimized with Honey Badger Algorithm (HBA) is proposed in this paper for Fetal Arrhythmia Detection and Classification from ECG signals (DSDACNN-HBA-FAC). The input ECG signals are collected from the fetal ECG dataset. Then, the ECG data are pre-processed using coherence shock filtering (CSF), which eliminates artifacts, like baseline drift, contact noise, muscle artifacts, electrode motion, electromyography artifacts. Using the improved non-subs sampled Shearlet transform (INSST) method, the pre-processed ECG data recover the mean, standard deviation, root mean square, skewness, kurtosis, zero-crossing, autoregressive coefficients, estimated entropy, and Higuchi’s fractal dimension. The DSDACNN classifier utilizes the features extracted to categorize the output as normal and arrhythmia. In general, DSDACNN does not agree on any optimization techniques to define the optimum parameters and to ensure accurate classification. Therefore, HBA is employed to optimize the DSDACNN weight parameters. The proposed method is implemented in MATLAB. The performance metrics, like sensitivity, precision, recall, f-measure, specificity, accuracy, computation time, RoC, and error rate are evaluated. The performance of DSDACNN-HBA-FAC achieves 3.101%, 7.12%, 7.73% high accuracy, 51.136%, 59.04%, 44.51% lower computation Time and 2.292%, 5.365%, 1.551% greater AUC compared with the existing techniques, like Fetal Arrhythmia Detection with Classification from ECG Signals: A NonInvasive Method (ANN-FAC), Semi-supervised active transfer learning for fetal ECG arrhythmia identification (DNN-FAC), fetal arrhythmia detection using adaptive single channel electrocardiogram extraction (CNN-FAC), respectively.

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Shock filter coupled with a high-order PDE for additive noise removal and image quality enhancement

Cited by 4 publications

References 17 publications

A Hybrid Filtering Technique of Digital Images in Multimedia Data Warehouses

A Hybrid Filtering Technique of Digital Images in Multimedia Data Warehouses

Parameter estimation and velocity signal extraction for one-dimensional wave equation with harmonic corrupted boundary observation

Deep Siamese Domain Adaptation Convolutional Neural Network Optimized with Honey Badger Algorithm for Fetal Arrhythmia Detection and Classification from ECG Signals

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