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Application and Research of Dark Channel Defogging Algorithm in Video Logging image Enhancement 6

Application and Research of Dark Channel Defogging Algorithm...

引用

6th IEEE international conference on Intelligent Computing and signal processing, ICSP 2021

作者： Dong, Haifeng Ma, Ruini Xi'an Shiyou University School of Computer Science Xi'an710065 China

ISBN: (纸本)9780738143705

The video logging technology is applied to detect the general condition of the well. Due to the influence of water fog, the contrast of the video logging image is decreased and the color contrast is distorted. The application and research of the dark channel dehazing algorithm in the video logging image enhancement system is proposed. In this paper, the dark channel defogging algorithm and Retinex defogging algorithm are compared and analyzed respectively, the realization process and application of the two algorithms are described briefly, and the advantages and disadvantages of the two algorithms applied in video logging image enhancement are compared. © 2021 IEEE.

关键词： image enhancement

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Impacts of Losses Functions on the Quality of the Ultrasound image by Using Machine Learning algorithms

Impacts of Losses Functions on the Quality of the Ultrasound...

引用

2021 IEEE international conference on Automatic Control and Intelligent Systems, I2CACIS 2021

作者： Dangoury, Soufiane Abouzahir, Saad Alali, Abdelhakim Sadik, Mohammed Hassan II University of Casablanca Morocco

ISBN: (纸本)9781665403436

During last decade, Artificial Intelligence (AI) has been able to reshape our life daily. Different areas were positively impacted by AI such as Healthcare, Logistic, etc. Medical imaging is one of the fields of healthcare in which AI was introduced to solve and overcome different problems. Challenges including image processing, signal processing, and data acquisition. In this paper, we deeply demonstrate the loss function as one of the main parameters that influence the quality of the ultrasound (US) image. Therefore, we introduce the main components of ultrasound systems form end-to-end perspective such as the data acquisition, the signal processing, and the image interpretation. Then, we present the losses functions as a critical performance metrics for the model validation. Metrics such as the Mean Absolute Error (MAE), Cross-Entropy loss function (CE), Dice Similarity Coefficient (DSC), and the Structural Similarity (SSIM). After that we present the adopted CNN model to generate ultrasound image. The excessive simulation results demonstrate that the selection of the loss function provides significant improvement in terms of image quality (e.g., contrast, CNR and SNR). Choosing simple loss functions such as mean square error helps to faster the convergence of the convolution neural network during the training process. However, for image quality enhancement, we propose the combination of different loss functions such structural similarity (SSIM) with Dice Similarity Coefficient (DSC). © 2021 IEEE.

关键词： Data acquisition

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Infrared image Face Recognition Method Based on signal Interference Technology 6th

Infrared Image Face Recognition Method Based on Signal Inter...

引用

6th European-Alliance-for-Innovation (EAI) international conference on advanced Hybrid Information processing (ADHIP)

作者： Chen, Zongren Xie, Wenda Guangdong Polytech Sci & Technol Artificial Intelligence Coll Comp Engn Tech Coll Zhuhai 519090 Peoples R China

ISBN: (纸本)9783031287862;9783031287879

Infrared imaging has the advantages of strong anti-interference, independent of visible light source, anti camouflage and anti fraud. Therefore, an infrared image face recognition method based on signal interference technology is proposed. Based on the in-depth analysis of the characteristics of infrared face image, the characteristics of infrared image face recognition are studied, and a new infrared image statistical face recognition method is proposed. The experimental results show that the recognition method studied in this paper is feasible both theoretically and experimentally, and has good recognition ability. The infrared image face recognition method can solve the difficult problem of face recognition under the change of lighting conditions and face camouflage.

关键词： signal interference technology Infrared image Face recognition Anti interference Visible light source

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Liver Tumor Segmentation Based on Three-Channel Cascaded SEU-Nets and C-LSTM 13

Liver Tumor Segmentation Based on Three-Channel Cascaded SEU...

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13th international conference on Wireless Communications and signal processing, WCSP 2021

作者： Li, Yarong Hu, Dong Nanjing University of Posts and Telecommunications Jiangsu Province's Key Lab of Image Processing and Image Communications Nanjing China

ISBN: (纸本)9781665407854

An abdominal CT liver tumor segmentation algorithm based on three-channel cascade SEU-Nets and C-LSTM is proposed in this paper. Concerning the accuracy of most known algorithms is limited by using single-phase CT images, the proposed method takes advantage of the different characteristics of the lesion area in the venous phase, arterial phase and delayed phase, and the correlation between slices in enhanced CT images. It uses three-channel SEU-Nets as the encoder to extract image features of slices in three phases, and uses 3D convolution to extract the relationship between slices for the same location at different phases. And then it uses C-LSTM to extract the inter-slice sequence feature relationship. Finally, the decoder outputs fine segmentation of liver tumor output. 40 sets of high-quality abdominal-enhanced CT images of the liver and liver tumors used in the experiment were collected in the provincial central hospital and annotated by experts. Comparisons are carried out with well-known methods, such as improved CFCNs, H-DenseUNet network and DBAs-based multi-scale DC-CUNets segmentation network. Experimental results show that the average segmentation accuracy of proposed algorithm is 7.04% higher than that of the multi-scale DC-CUNets segmentation network based on DBAs, and the DICE coefficient is improved by 4.89%. © 2021 IEEE.

关键词： Long short-term memory

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2021 international conference in Advances in Power, signal, and Information Technology, APSIT 2021

2021 International Conference in Advances in Power, Signal, ...

引用

2021 international conference in Advances in Power, signal, and Information Technology, APSIT 2021

ISBN: (纸本)9781665425063

The proceedings contain 109 papers. The topics discussed include: a hybrid approach of deep learning algorithms for identification of COVID-19 disease using chest x-ray images;image encryption using chaotic techniques: a survey study;performance evaluation of photovoltaic based distributed generation against neural network based feedback control;solution to economic dispatch problem incorporating micro grid with integration of wind energy using emperor penguin optimization algorithm;an enhanced dynamic image watermarking framework based on phase congruency and saliency;review of blockchain-based energy trading models;breast cancer data analysis using machine learning approaches;and advanced reactive power control technique for wind power application.

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Towards Automatic Detection and Quantification of Mildew on Grape Leaf Disks 18

Towards Automatic Detection and Quantification of Mildew on ...

引用

18th international conference on signal processing and Multimedia Applications (SIGMAP)

作者： Iqbal, Razib Sargent, Kyle Kovacs, Laszlo Missouri State Univ Coll Nat & Appl Sci Springfield MO 65897 USA

ISBN: (纸本)9789897585258

Downy and powdery mildews are the most serious diseases of the grapevine. A sustainable way to control these pathogens is the breeding and deployment of resistant grape cultivars. For breeding efforts to be effective, accurate quantification of the resistance phenotype is essential. In this paper, we present a computerbased image recognition, processing, and analysis technique for enhancing the detection and quantification of Plasmopara viticola and Erysiphe necator the causal agents of downy and powdery mildew, respectively. We propose a multi-step approach that utilizes background removal and Hue-Saturation-Value (HSV) masking as opposed to multi-faceted color channel breakdowns, photo texture evaluations, or classification-based algorithms for the detection of mildew. Our experimental results show that our method provides reliable results and fast performance.

关键词： Background Removal Downy Mildew Grape Leaf HSV Masking image Analysis

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A LIGHTWEIGHT RELU-BASED FEATURE FUSION FOR AERIAL SCENE CLASSIFICATION

A LIGHTWEIGHT RELU-BASED FEATURE FUSION FOR AERIAL SCENE CLA...

引用

IEEE international conference on image processing (ICIP)

作者： Arefeen, Md Adnan Nimi, Sumaiya Tabassum Uddin, Md Yusuf Sarwar Li, Zhu Univ Missouri Kansas City Dept EECS Kansas City MO 64110 USA

ISBN: (纸本)9781665441155

In this paper, we propose a transfer-learning based model construction technique for the aerial scene classification problem. The core of our technique is a layer selection strategy, named ReLU-Based Feature Fusion (RBFF), that extracts feature maps from a pretrained CNN-based single-object image classification model, namely MobileNetV2, and constructs a model for the aerial scene classification task. RBFF stacks features extracted from the batch normalization layer of a few selected blocks of MobileNetV2, where the candidate blocks are selected based on the characteristics of the ReLU activation layers present in those blocks. The feature vector is then compressed into a low-dimensional feature space using dimension reduction algorithms on which we train a low-cost SVM classifier for the classification of the aerial images. We validate our choice of selected features based on the significance of the extracted features with respect to our classification pipeline. RBFF remarkably does not involve any training of the base CNN model except for a few parameters for the classifier, which makes the technique very cost-effective for practical deployments. The constructed model despite being lightweight outperforms several recently proposed models in terms of accuracy for a number of aerial scene datasets.

关键词： Layer selection Aerial image analysis Transfer learning Feature fusion image classification

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Analysis of Deep Learning algorithms for image Denoising 1

引用

1st international conference on Advances in signal processing and Communication Engineering (ICASPACE)

作者： Choudhary, Nikita Sharma, Rakesh Natl Inst Technol Hamirpur Elect & Commun Engn Dept Hamirpur 177005 Himachal Prades India

ISBN: (数字)9789811955501

ISBN: (纸本)9789811955525;9789811955501;9789811955495

A deep learning-based image denoising model for removing non-additive non-gaussian noise from images is proposed in this paper. images are considered unstructured data, and these are easily corrupted in the complex environment. Due to the noise in the digital images, poor image quality-related issues arise in the images. image noise reduction is an issue in the realm of image processing. Learning-based algorithms for image denoising have gotten a lot of focus because of their superior denoising performance. Deep learning models are neural networks that have multi-layer structures. These structures are very helpful for extracting very complex features from input images and then remove noise. An extensive database of images is used to train and validate the model. The qualitative evaluation based on visual quality is used to assess the denoised images. The quantitative assessment with mean squared error, peak signal to noise ratio, and structural similarity index measure is used to evaluate the denoising outcomes.

关键词： image denoising Deep learning (DL) Convolutional neural network (CNN) Optimization method Mean squared error (MSE) Peak signal to noise ratio (PSNR) Structural similarity index measure (SSIM)

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NUCLEAR DENSITY DISTRIBUTION FEATURE FOR IMPROVING CERVICAL HISTOPATHOLOGICAL imageS RECOGNITION

NUCLEAR DENSITY DISTRIBUTION FEATURE FOR IMPROVING CERVICAL ...

引用

IEEE international conference on image processing (ICIP)

作者： Wang, Zhuangzhuang Yang, Mengning Lyu, Yangfan Chen, Kairun Tang, Qicheng Chongqing Univ Sch Big Data & Software Engn Chongqing Peoples R China Army Med Univ Affiliated Hosp 2 Dept Pathol Chongqing Peoples R China

ISBN: (纸本)9781665441155

Cervical carcinoma is a common type of cancer in the female reproductive system. Early detection and diagnosis can facilitate immediate treatment and prevent progression of the disease. However, in order to achieve better performance, DL-based algorithms just stack various layers with low interpretability. In this paper, a robust and reliable Nuclear Density Distribution Feature (NDDF) based on priors of the pathologists to promote the Cervical Histopathological image Classification (CHIC) is proposed. Our proposed method combines the nucleus mask segmented by U-Net with the segmentation grid-lines generated from pathology images utilizing SLIC to obtain the NDDF map, which contains information about the morphology, size, number, and spatial distribution of nuclei. The result shows that the proposed model trained with NDDF maps has better performance and accuracy than that trained on RGB images (patch-level histopathological images). More significantly, the accuracy of the two-stream network trained with RGB images and NDDF maps is steadily improved over the corresponding baselines of different complexity.

关键词： Nuclear Density Distribution Superpixel Deep Learning Histopathological image Classification

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BUILDING FOOTPRINT GENERATION BY INTEGRATING U-NET WITH DEEPENED SPACE MODULE

BUILDING FOOTPRINT GENERATION BY INTEGRATING U-NET WITH DEEP...

引用

IEEE international conference on image processing (ICIP)

作者： Chen, Jun Jiang, Yuxuan Luo, Linbo Gu, Yue Wu, Kangle China Univ Geosci Sch Automat Wuhan Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan Peoples R China China Univ Geosci Sch Mech Engn & Elect Informat Wuhan Peoples R China

ISBN: (纸本)9781665441155

In this paper, we propose a novel and practical convolutional neural network method for building footprint generation in remote sensing images, in order to deal with the problem that the detailed information and geometric structure of ground objects in high-resolution images become more abundant, which leads to a large increase in the calculation amount. So we introduce a deepened space module, which can ignore the channels with weak target features and emphasize the effective features. It is embedded in each splicing layer in the upsampling process of U-net to achieve the effect of feature selection. By means of clipping and data enhancement, we carry out iterative training and model optimization learning on Inria aerial image label dataset, and realize the automatic generation of building footprint. Compared with FCN8s, Unet, SegNet, PSPNet, Deeplabv3+ and GLNet, experimental results show that the method we use to generate building footprint is more accurate, and in IoU, mPA, PA three indicators are better than the comparison algorithms.

关键词： deep learning semantic segmentation high resolution image

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