Image denoising algorithm based on wavelet transform and  convolutional neural network

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

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Methods for image denoising using convolutional neural network: a review

Complex & Intelligent Systems ◽

10.1007/s40747-021-00428-4 ◽

2021 ◽

Author(s):

Ademola E. Ilesanmi ◽

Taiwo O. Ilesanmi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Image Denoising ◽

Speckle Noise ◽

Future Research ◽

The Arts

AbstractImage denoising faces significant challenges, arising from the sources of noise. Specifically, Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in imaging. Convolutional neural network (CNN) has increasingly received attention in image denoising task. Several CNN methods for denoising images have been studied. These methods used different datasets for evaluation. In this paper, we offer an elaborate study on different CNN techniques used in image denoising. Different CNN methods for image denoising were categorized and analyzed. Popular datasets used for evaluating CNN image denoising methods were investigated. Several CNN image denoising papers were selected for review and analysis. Motivations and principles of CNN methods were outlined. Some state-of-the-arts CNN image denoising methods were depicted in graphical forms, while other methods were elaborately explained. We proposed a review of image denoising with CNN. Previous and recent papers on image denoising with CNN were selected. Potential challenges and directions for future research were equally fully explicated.

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Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network

Knowledge-Based Systems ◽

10.1016/j.knosys.2021.106796 ◽

2021 ◽

Vol 216 ◽

pp. 106796

Author(s):

Yiwei Cheng ◽

Manxi Lin ◽

Jun Wu ◽

Haiping Zhu ◽

Xinyu Shao

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Continuous Wavelet Transform ◽

Rotating Machinery ◽

Continuous Wavelet ◽

Intelligent Fault Diagnosis

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An efficient epileptic seizure classification system using empirical wavelet transform and multi-fuse reduced deep convolutional neural network with digital implementation

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2021.103281 ◽

2022 ◽

Vol 72 ◽

pp. 103281

Author(s):

Susanta Kumar Rout ◽

Mrutyunjaya Sahani ◽

Chinmayee Dora ◽

Pradyut Kumar Biswal ◽

Birendra Biswal

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Convolutional Neural Network ◽

Epileptic Seizure ◽

Classification System ◽

Deep Convolutional Neural Network ◽

Digital Implementation ◽

Empirical Wavelet Transform ◽

Seizure Classification

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A multi-objective opposition-based barnacles mating optimization for image super resolution using hyper-Spectral images

Journal of Engineering Design and Technology ◽

10.1108/jedt-01-2021-0030 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Valli Bhasha A. ◽

Venkatramana Reddy B.D.

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Convolutional Neural Network ◽

Super Resolution ◽

Discrete Wavelet ◽

Content Type ◽

Resolution Model ◽

Deep Cnn ◽

Image Super Resolution ◽

Better Than

Purpose The problems of Super resolution are broadly discussed in diverse fields. Rather than the progression toward the super resolution models for real-time images, operating hyperspectral images still remains a challenging problem. Design/methodology/approach This paper aims to develop the enhanced image super-resolution model using “optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT), and Optimized Deep Convolutional Neural Network”. Once after converting the HR images into LR images, the NSSR images are generated by the optimized NSSR. Then the ADWT is used for generating the subbands of both NSSR and HRSB images. The residual image with this information is obtained by the optimized Deep CNN. All the improvements on the algorithms are done by the Opposition-based Barnacles Mating Optimization (O-BMO), with the objective of attaining the multi-objective function concerning the “Peak Signal-to-Noise Ratio (PSNR), and Structural similarity (SSIM) index”. Extensive analysis on benchmark hyperspectral image datasets shows that the proposed model achieves superior performance over typical other existing super-resolution models. Findings From the analysis, the overall analysis of the suggested and the conventional super resolution models relies that the PSNR of the improved O-BMO-(NSSR+DWT+CNN) was 38.8% better than bicubic, 11% better than NSSR, 16.7% better than DWT+CNN, 1.3% better than NSSR+DWT+CNN, and 0.5% better than NSSR+FF-SHO-(DWT+CNN). Hence, it has been confirmed that the developed O-BMO-(NSSR+DWT+CNN) is performing well in converting LR images to HR images. Originality/value This paper adopts a latest optimization algorithm called O-BMO with optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT) and Optimized Deep Convolutional Neural Network for developing the enhanced image super-resolution model. This is the first work that uses O-BMO-based Deep CNN for image super-resolution model enhancement.

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Crack Grid Detection and Calculation Based on Convolutional Neural Network

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2020-0246 ◽

2021 ◽

Author(s):

Liyang Xiao ◽

Wei Li ◽

Ju Huyan ◽

Zhaoyun Sun ◽

Susan Tighe

Keyword(s):

Neural Network ◽

Image Quality ◽

Convolutional Neural Network ◽

Image Denoising ◽

Crack Detection ◽

Experimental Results ◽

Crack Identification ◽

Threshold Segmentation ◽

Improve Image Quality

This paper aims to develop a method of crack grid detection based on convolutional neural network. First, an image denoising operation is conducted to improve image quality. Next, the processed images are divided into grids of different, and each grid is fed into a convolutional neural network for detection. The pieces of the grids with cracks are marked and then returned to the original images. Finally, on the basis of the detection results, threshold segmentation is performed only on the marked grids. Information about the crack parameters is obtained via pixel scanning and calculation, which realises complete crack detection. The experimental results show that 30×30 grids perform the best with the accuracy value of 97.33%. The advantage of automatic crack grid detection is that it can avoid fracture phenomenon in crack identification and ensure the integrity of cracks.

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