Deep-learning-based electrical noise removal for localized spectral optoacoustic contrast in deep tissue

2021 ◽  
Author(s):  
Christoph Dehner ◽  
Ivan Olefir ◽  
Kaushik B. Chowdhury ◽  
Dominik Jüstel ◽  
Vasilis Ntziachristos
Keyword(s):  
Deep Learning ◽  
Noise Removal ◽  
Electrical Noise ◽  
Deep Tissue

scholarly journals Deep Learning Based Depthwise Separable Model For Effective Diagnosis And Classification of Lung Ct Images

2019 ◽  
Vol 9 (1) ◽  
pp. 1808-1819 ◽  
Keyword(s):  
Lung Cancer ◽  
Feature Extraction ◽  
Deep Learning ◽  
Noise Removal ◽  
Ct Images ◽  
Classification Model ◽  
Diagnosis And Classification ◽  
Effective Diagnosis ◽  
Lung Ct

Lung cancer is a serious illness which leads to increased mortality rate globally. The identification of lung cancer at the beginning stage is the probable method of improving the survival rate of the patients. Generally, Computed Tomography (CT) scan is applied for finding the location of the tumor and determines the stage of cancer. Existing works has presented an effective diagnosis classification model for CT lung images. This paper designs an effective diagnosis and classification model for CT lung images. The presented model involves different stages namely pre-processing, segmentation, feature extraction and classification. The initial stage includes an adaptive histogram based equalization (AHE) model for image enhancement and bilateral filtering (BF) model for noise removal. The pre-processed images are fed into the second stage of watershed segmentation model for effectively segment the images. Then, a deep learning based Xception model is applied for prominent feature extraction and the classification takes place by the use of logistic regression (LR) classifier. A comprehensive simulation is carried out to ensure the effective classification of the lung CT images using a benchmark dataset. The outcome implied the outstanding performance of the presented model on the applied test images.

scholarly journals Deep learning algorithm for Gaussian noise removal from images

2020 ◽  
Vol 29 (04) ◽  
pp. 1
Author(s):  
Mickael Aghajarian ◽  
John E. McInroy ◽  
Suresh Muknahallipatna
Keyword(s):  
Deep Learning ◽  
Gaussian Noise ◽  
Learning Algorithm ◽  
Noise Removal ◽  
Deep Learning Algorithm

scholarly journals CAPSULE NETWORK BASED BIOMETRIC RECOGNITION SYSTEM

2019 ◽  
Vol 2019 (2) ◽  
pp. 83-94 ◽  
Author(s):  
Dr. I. Jeena Jacob
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Recognition System ◽  
Noise Removal ◽  
Personal Identification ◽  
Biometric Recognition ◽  
Learning Techniques ◽  
Frame Work ◽  
The Stability

The biometric recognition plays a significant and a unique part in the applications that are based on the personal identification. This is because of the stability, irreplaceability and the uniqueness that is found in the biometric traits of the humans. Currently the deep learning techniques that are capable of strongly generalizing and automatically learning, with the enhanced accuracy is utilized for the biometric recognition to develop an efficient biometric system. But the poor noise removal abilities and the accuracy degradation caused due to the very small disturbances has made the conventional means of the deep learning that utilizes the convolutional neural network incompatible for the biometric recognition. So the capsule neural network replaces the CNN due to its high accuracy in the recognition and the classification, due to its learning capacities and the ability to be trained with the limited number of samples compared to the CNN (convolutional neural network). The frame work put forward in the paper utilizes the capsule network with the fuzzified image enhancement for the retina based biometric recognition as it is a highly secure and reliable basis of person identification as it is layered behind the eye and cannot be counterfeited. The method was tested with the dataset of face 95 database and the CASIA-Iris-Thousand, and was found to be 99% accurate with the error rate convergence of 0.3% to .5%

Background Noise Removal Technique using Deep Learning Segmentation Network without Segmentation Map

2021 ◽  
Vol 263 (2) ◽  
pp. 4441-4445
Author(s):  
Hyunsuk Huh ◽  
Seungchul Lee
Keyword(s):  
Deep Learning ◽  
Background Noise ◽  
Ground Truth ◽  
Noise Removal ◽  
Industrial Manufacturing ◽  
Proposed Model ◽  
Audio Data ◽  
Passive Noise ◽  
Removal Technique ◽  
Noise Canceling

Audio data acquired at industrial manufacturing sites often include unexpected background noise. Since the performance of data-driven models can be worse by background noise. Therefore, it is important to get rid of unwanted background noise. There are two main techniques for noise canceling in a traditional manner. One is Active Noise Canceling (ANC), which generates an inverted phase of the sound that we want to remove. The other is Passive Noise Canceling (PNC), which physically blocks the noise. However, these methods require large device size and expensive cost. Thus, we propose a deep learning-based noise canceling method. This technique was developed using audio imaging technique and deep learning segmentation network. However, the proposed model only needs the information on whether the audio contains noise or not. In other words, unlike the general segmentation technique, a pixel-wise ground truth segmentation map is not required for this method. We demonstrate to evaluate the separation using pump sound of MIMII dataset, which is open-source dataset.

scholarly journals Deep Learning Based Switching Filter for Impulsive Noise Removal in Color Images

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2782
Author(s):  
Krystian Radlak ◽  
Lukasz Malinski ◽  
Bogdan Smolka
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Network Architecture ◽  
Noise Suppression ◽  
Impulsive Noise ◽  
Noise Removal ◽  
Vision Systems ◽  
Mean Filter ◽  
Computer Vision Systems ◽  
Active Research

Noise reduction is one of the most important and still active research topics in low-level image processing due to its high impact on object detection and scene understanding for computer vision systems. Recently, we observed a substantially increased interest in the application of deep learning algorithms. Many computer vision systems use them, due to their impressive capability of feature extraction and classification. While these methods have also been successfully applied in image denoising, significantly improving its performance, most of the proposed approaches were designed for Gaussian noise suppression. In this paper, we present a switching filtering technique intended for impulsive noise removal using deep learning. In the proposed method, the distorted pixels are detected using a deep neural network architecture and restored with the fast adaptive mean filter. The performed experiments show that the proposed approach is superior to the state-of-the-art filters designed for impulsive noise removal in color digital images.

scholarly journals An Artificial Intelligence Framework for Plant Leaf Disease Detection and Classification Using AMBF with GKFCM and GLCM

2021 ◽  
Vol 36 (1) ◽  
pp. 443-450
Author(s):  
Mounika Jammula
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Plant Disease ◽  
Bilateral Filter ◽  
Noise Removal ◽  
Classification Performance ◽  
Disease Classification ◽  
Gaussian Kernel ◽  
Shape Features

As of 2020, the total area planted with crops in India overtook 125.78 million hectares. India is the second biggest organic product maker in the world. Thus, an Indian economy greatly depends on farming products. Nowadays, farmers suffer a drop in production due to a lot of diseases and pests. Thus, to overcome this problem, this article presents the artificial intelligence based deep learning approach for plant disease classification. Initially, the adaptive mean bilateral filter (AMBF) for noise removal and enhancement operations. Then, Gaussian kernel fuzzy C-means (GKFCM) approach is used to segment the effected disease regions. The optimal features from color, texture and shape features are extracted by using GLCM. Finally, Deep learning convolutional neural network (DLCNN) is used for the classification of five class diseases. The segmentation and classification performance of proposed method outperforms as compared with the state of art approaches.

scholarly journals Depth Data Denoising in Optical Laser Based Sensors for Metal Sheet Flatness Measurement: A Deep Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7024
Author(s):  
Marcos Alonso ◽  
Daniel Maestro ◽  
Alberto Izaguirre ◽  
Imanol Andonegui ◽  
Manuel Graña
Keyword(s):  
Deep Learning ◽  
Optical Sensors ◽  
Optical Measurement ◽  
Image Data ◽  
Real Data ◽  
Noise Removal ◽  
Metal Sheet ◽  
Superior Performance ◽  
Range Image ◽  
Range Images

Surface flatness assessment is necessary for quality control of metal sheets manufactured from steel coils by roll leveling and cutting. Mechanical-contact-based flatness sensors are being replaced by modern laser-based optical sensors that deliver accurate and dense reconstruction of metal sheet surfaces for flatness index computation. However, the surface range images captured by these optical sensors are corrupted by very specific kinds of noise due to vibrations caused by mechanical processes like degreasing, cleaning, polishing, shearing, and transporting roll systems. Therefore, high-quality flatness optical measurement systems strongly depend on the quality of image denoising methods applied to extract the true surface height image. This paper presents a deep learning architecture for removing these specific kinds of noise from the range images obtained by a laser based range sensor installed in a rolling and shearing line, in order to allow accurate flatness measurements from the clean range images. The proposed convolutional blind residual denoising network (CBRDNet) is composed of a noise estimation module and a noise removal module implemented by specific adaptation of semantic convolutional neural networks. The CBRDNet is validated on both synthetic and real noisy range image data that exhibit the most critical kinds of noise that arise throughout the metal sheet production process. Real data were obtained from a single laser line triangulation flatness sensor installed in a roll leveling and cut to length line. Computational experiments over both synthetic and real datasets clearly demonstrate that CBRDNet achieves superior performance in comparison to traditional 1D and 2D filtering methods, and state-of-the-art CNN-based denoising techniques. The experimental validation results show a reduction in error than can be up to 15% relative to solutions based on traditional 1D and 2D filtering methods and between 10% and 3% relative to the other deep learning denoising architectures recently reported in the literature.

scholarly journals The Discrete Wavelet Transform and Its Application for Noise Removal in Localized Corrosion Measurements

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Rogelio Ramos ◽  
Benjamin Valdez-Salas ◽  
Roumen Zlatev ◽  
Michael Schorr Wiener ◽  
Jose María Bastidas Rull
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Electrical Potential ◽  
Digital Processing ◽  
Noise Removal ◽  
Localized Corrosion ◽  
Electrical Noise ◽  
Discrete Wavelet ◽  
Electrical Measurements ◽  
Threshold Method

The present work discusses the problem of induced external electrical noise as well as its removal from the electrical potential obtained from Scanning Vibrating Electrode Technique (SVET) in the pitting corrosion process of aluminum alloy A96061 in 3.5% NaCl. An accessible and efficient solution of this problem is presented with the use of virtual instrumentation (VI), embedded systems, and the discrete wavelet transform (DWT). The DWT is a computational algorithm for digital processing that allows obtaining electrical noise with Signal to Noise Ratio (SNR) superior to those obtained with Lock-In Amplifier equipment. The results show that DWT and the threshold method are efficient and powerful alternatives to carry out electrical measurements of potential signals from localized corrosion processes measured by SVET.

scholarly journals Customized Synthetic Dataset for Deep Learning Noise Filtering for Time-of-Flight Indoor Navigation Applications

2020 ◽  
Author(s):  
Vinícius da Silva Ramalho ◽  
Rômulo Francisco Lepinsk Lopes ◽  
Ricardo Luhm Silva ◽  
Marcelo Rudek
Keyword(s):  
Deep Learning ◽  
Random Noise ◽  
Time Of Flight ◽  
Ground Truth ◽  
Noise Removal ◽  
Synthetic Dataset ◽  
Ground Truth Data ◽  
Performance Benchmarking ◽  
Depth Images ◽  
Synthetic Datasets

Synthetic datasets have been used to train 2D and 3D image-based deep learning models, and they serve as also as performance benchmarking. Although some authors already use 3D models for the development of navigation systems, their applications do not consider noise sources, which affects 3D sensors. Time-of-Flight sensors are susceptible to noise and conventional filters have limitations depending on the scenario it will be applied. On the other hand, deep learning filters can be more invariant to changes and take into consideration contextual information to attenuate noise. However, to train a deep learning filter a noiseless ground truth is required, but highly accurate hardware would be need. Synthetic datasets are provided with ground truth data, and similar noise can be applied to it, creating a noisy dataset for a deep learning approach. This research explores the training of a noise removal application using deep learning trained only with the Flying Things synthetic dataset with ground truth data and applying random noise to it. The trained model is validated with the Middlebury dataset which contains real-world data. The research results show that training the deep learning architecture for noise removal with only a synthetic dataset is capable to achieve near state of art performance, and the proposed model is able to process 12bit resolution depth images instead of 8bit images. Future studies will evaluate the algorithm performance regarding real-time noise removal to allow embedded applications.

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