scholarly journals Convolutional Neural Network Based Image Denoising for Better Quality of Images

2018 ◽  
Vol 7 (3.27) ◽  
pp. 356
Author(s):  
Anandbabu Gopatoti ◽  
Merajothu Chandra Naik ◽  
Kiran Kumar Gopathoti
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Denoising ◽  
Wavelet Transforms ◽  
Visual Information ◽  
Wavelet Coefficient ◽  
Image Data ◽  
Vital Role ◽  
Adaptive Wavelet ◽  
Comparative Accuracy

This work gives a survey by comparing the different methods of image denoising with the help of wavelet transforms and Convolutional Neural Network. To get the better method for Image denoising, there is distinctive merging which have been used. The vital role of communication is transmitting visual information in the appearance of digital images, but on the receiver side we will get the image with corruption. Therefore, in practical analysis and facts, the powerful image denoising approach is still a legitimate undertaking. The algorithms which are very beneficial for processing the signal like compression of image and denoising the image is Wavelet transforms. To get a better quality image as output, denoising methods includes the maneuver of data of that image. The primary aim is wavelet coefficient modification inside the new basis, by that the noise within the image data can be eliminated. In this paper, we suggested different methods of image denoising from the corrupted images with the help of different noises like Gaussian and speckle noises. This paper implemented by using adaptive wavelet threshold( Sure Shrink, Block Shrink, Neigh Shrink and  Bivariate Shrink) and Convolutional Neural Network(CNN) Model, the experimental consequences the comparative accuracy of our proposed work.  

scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

scholarly journals Methods for image denoising using convolutional neural network: a review

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.

scholarly journals Lightweight Convolutional Neural Network Based Intrusion Detection System

2020 ◽  
pp. 808-817
Author(s):  
Vinh Pham ◽  
◽  
Eunil Seo ◽  
Tai-Myoung Chung
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Network Traffic ◽  
Detection System ◽  
Image Data ◽  
Training Data ◽  
Deep Packet Inspection ◽  
Detection Model

Identifying threats contained within encrypted network traffic poses a great challenge to Intrusion Detection Systems (IDS). Because traditional approaches like deep packet inspection could not operate on encrypted network traffic, machine learning-based IDS is a promising solution. However, machine learning-based IDS requires enormous amounts of statistical data based on network traffic flow as input data and also demands high computing power for processing, but is slow in detecting intrusions. We propose a lightweight IDS that transforms raw network traffic into representation images. We begin by inspecting the characteristics of malicious network traffic of the CSE-CIC-IDS2018 dataset. We then adapt methods for effectively representing those characteristics into image data. A Convolutional Neural Network (CNN) based detection model is used to identify malicious traffic underlying within image data. To demonstrate the feasibility of the proposed lightweight IDS, we conduct three simulations on two datasets that contain encrypted traffic with current network attack scenarios. The experiment results show that our proposed IDS is capable of achieving 95% accuracy with a reasonable detection time while requiring relatively small size training data.

scholarly journals Image denoising algorithm based on wavelet transform and convolutional neural network

2020 ◽  
Vol 41 (2) ◽  
pp. 288-295
Author(s):  
CHEN Qingjiang ◽  
◽  
◽  
SHI Xiaohan ◽  
CHAI Yuzhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Image Denoising

Crack Grid Detection and Calculation Based on Convolutional Neural Network

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.

scholarly journals Inferring Emotion Tags from Object Images Using Convolutional Neural Network

2020 ◽  
Vol 10 (15) ◽  
pp. 5333
Author(s):  
Anam Manzoor ◽  
Waqar Ahmad ◽  
Muhammad Ehatisham-ul-Haq ◽  
Abdul Hannan ◽  
Muhammad Asif Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Human Behavior ◽  
Real Life ◽  
Vital Role ◽  
Object Categories ◽  
Different Types ◽  
Gender Based ◽  
High Level ◽  
Human Behavior Analysis

Emotions are a fundamental part of human behavior and can be stimulated in numerous ways. In real-life, we come across different types of objects such as cake, crab, television, trees, etc., in our routine life, which may excite certain emotions. Likewise, object images that we see and share on different platforms are also capable of expressing or inducing human emotions. Inferring emotion tags from these object images has great significance as it can play a vital role in recommendation systems, image retrieval, human behavior analysis and, advertisement applications. The existing schemes for emotion tag perception are based on the visual features, like color and texture of an image, which are poorly affected by lightning conditions. The main objective of our proposed study is to address this problem by introducing a novel idea of inferring emotion tags from the images based on object-related features. In this aspect, we first created an emotion-tagged dataset from the publicly available object detection dataset (i.e., “Caltech-256”) using subject evaluation from 212 users. Next, we used a convolutional neural network-based model to automatically extract the high-level features from object images for recognizing nine (09) emotion categories, such as amusement, awe, anger, boredom, contentment, disgust, excitement, fear, and sadness. Experimental results on our emotion-tagged dataset endorse the success of our proposed idea in terms of accuracy, precision, recall, specificity, and F1-score. Overall, the proposed scheme achieved an accuracy rate of approximately 85% and 79% using top-level and bottom-level emotion tagging, respectively. We also performed a gender-based analysis for inferring emotion tags and observed that male and female subjects have discernment in emotions perception concerning different object categories.

scholarly journals Underground Cylindrical Objects Detection and Diameter Identification in GPR B-Scans via the CNN-LSTM Framework

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1804
Author(s):  
Wentai Lei ◽  
Jiabin Luo ◽  
Feifei Hou ◽  
Long Xu ◽  
Ruiqing Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Vital Role ◽  
Short Term ◽  
Term Memory ◽  
Region Detection ◽  
Field Environment ◽  
Long Short Term Memory ◽  
Lstm Network

Ground penetrating radar (GPR), as a non-invasive instrument, has been widely used in the civil field. The interpretation of GPR data plays a vital role in underground infrastructures to transfer raw data to the interested information, such as diameter. However, the diameter identification of objects in GPR B-scans is a tedious and labor-intensive task, which limits the further application in the field environment. The paper proposes a deep learning-based scheme to solve the issue. First, an adaptive target region detection (ATRD) algorithm is proposed to extract the regions from B-scans that contain hyperbolic signatures. Then, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework is developed that integrates Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to extract hyperbola region features. It transfers the task of diameter identification into a task of hyperbola region classification. Experimental results conducted on both simulated and field datasets demonstrate that the proposed scheme has a promising performance for diameter identification. The CNN-LSTM framework achieves an accuracy of 99.5% on simulated datasets and 92.5% on field datasets.

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