Super resolution reconstruction of human face is a cost effective way to obtain high resolution images from its corresponding low resolution face. It is also known as face illusion. In order to obtain clearer texture ...
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ISBN:
(数字)9781728151694
ISBN:
(纸本)9781728151700
Super resolution reconstruction of human face is a cost effective way to obtain high resolution images from its corresponding low resolution face. It is also known as face illusion. In order to obtain clearer texture details, this paper proposes a densely connected super-resolution algorithm based on attention mechanism which consists of feature extraction and image reconstruction. By integrating channel and spatial domain information of the feature map, the Multi Attention Domain Module (MADM) is proposed: Features are weighted and recombined by analyzing the relationship between channels and spatial information of feature maps. The features of different layers are fused using dense connections. Experimental results show that the proposed algorithm can improve by up to 0.5dB in PSNR and the reconstructed face image has clearer texture details compared to existing algorithms.
After many years of research, optical flow algorithm has achieved good results in detecting moving objects in simple scenes, but the detection effect in some complex scenes is not ideal, for example, in scenes with ch...
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In this paper, a two-dimensional truth-oriented brain model is proposed for imaging simulation of wearable stroke detection devices. Stroke detection based on ultra-wideband microwave imaging is a new detection method...
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ISBN:
(纸本)9781665444927
In this paper, a two-dimensional truth-oriented brain model is proposed for imaging simulation of wearable stroke detection devices. Stroke detection based on ultra-wideband microwave imaging is a new detection method, which is portable, fast and safe. Wearable detection equipment is an important development direction of ultra-wideband microwave imaging for stroke. By analyzing the electromagnetic characteristics of the brain tissue at different frequencies, the electromagnetic parameters of the brain tissue were determined, and the real human brain MRI images were used for analysis and modeling. Compared to previous simple brain model, this model can more truly reflect the internal structure of human brain, and provide a more accurate experimental platform for various imaging simulation algorithms. Finally, the delay confocal algorithm was used to reconstruct the brain image. In the imaging results, the bleeding spots with a diameter of 6 mm in the brain is clearly identified.
Optical location systems implemented on the basis of high-resolution video cameras are currently used in many areas. These are, for example, medical equipment, traffic control systems, satellite monitoring systems, pr...
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ISBN:
(数字)9781510631120
ISBN:
(纸本)9781510631120
Optical location systems implemented on the basis of high-resolution video cameras are currently used in many areas. These are, for example, medical equipment, traffic control systems, satellite monitoring systems, preventive security systems, object recognition and classification systems, etc. For these systems, the requirements for high-resolution imageprocessing speed of 8K, 16K and more are increasing every year. processing of such information becomes even more difficult when providing a high frequency of reading frames from the matrix of the video camera, especially for systems operating in real time and using high-speed networks of exchange, processing and integration of information. This requires to determine a set of types of information processing procedures: masking, compression, noise-protected coding, etc., for which algorithms should be revised in case of multi-user and multiposition application in distributed information processing and aggregation systems. In this regard, the problems of development and improvement of new ways of representation, compression, storage, masking and error-correcting coding of high-resolution images with a common mathematical basis are relevant. Most information transformation procedures are based on the use of orthogonal bases, in particular orthogonal and quasiorthogonal matrices. The paper presents the results of the search and formation of such bases, the methods of synthesis of quasi-orthogonal matrices for imageprocessing problems that meet the formulated requirements. The methods of guaranteed synthesis of matrices of symmetric, cyclic, block-cyclic and other structures of different orders, assuming economical storage and generation, are proposed. Such matrix bases, which are constantly expanding, provide developers with a wide range of algorithms to choose the most appropriate one from them. The problem of search and study of extreme quasi-orthogonal matrices has great importance for a wider range of information p
With the development of medical technology, the automatic cell analysis system plays an important role in medical diagnosis and medical imageprocessing. The kernel recognition theory and technology based on support v...
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ISBN:
(数字)9781728161365
ISBN:
(纸本)9781728161372
With the development of medical technology, the automatic cell analysis system plays an important role in medical diagnosis and medical imageprocessing. The kernel recognition theory and technology based on support vector machine (SVM) classifier are mainly optimized from the perspective of the kernel segmentation algorithm to improve the recognition accuracy of the SVM classifier. Unfortunately, the nuclear overlap treatment can not accurately separate the nuclear gelling impurities in the dyeing process, resulting in the low classification accuracy of SVM. To solve the above image segmentation problems in the process of nuclear imaging processing, an effective nuclear extraction method based on the mask method for the SVM classifier is proposed. Compared with related work, the proposed method enables one to achieve a higher accuracy of SVM cross-validation.
Eczema is the most common among all types of skin diseases. A solution for this disease is very crucial for patients to have better treatment. Eczema is usually detected manually by doctors or dermatologists. It is to...
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ISBN:
(数字)9781728175744
ISBN:
(纸本)9781728175751
Eczema is the most common among all types of skin diseases. A solution for this disease is very crucial for patients to have better treatment. Eczema is usually detected manually by doctors or dermatologists. It is tough to distinguish between different types of Eczema because of the similarities in symptoms. In recent years, several attempts have been taken to automate the detection of skin diseases with much accuracy. Many methods such as imageprocessing Techniques, Machine Learning algorithms are getting used to execute segmentation and classification of skin diseases. It is found that among all those skin disease detection systems, particularly detection work on eczema disease is rare. There is also insufficiency in eczema disease dataset. In this paper, we propose a novel deep CNN-based approach for classifying five different classes of Eczema with our collected dataset. Data augmentation is used to transform images for better performance. Regularization techniques such as batch normalization and dropout helped to reduce overfitting. Our proposed model achieved an accuracy of 96.2%, which exceeded the performance of the state of the arts.
The issues of enhanced vision multispectral systems application for robotic complexes control in the Arctic are considered. The existing contrast enhancement methods are observed. Probability characteristics of images...
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A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are mostly trained to detect a specific fake method. As a result, these approaches show poor generalization across different types of...
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ISBN:
(纸本)9781665428132
A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are mostly trained to detect a specific fake method. As a result, these approaches show poor generalization across different types of facial manipulations, e.g., from face swapping to facial reenactment. To this end, we introduce ID-Reveal, a new approach that learns temporal facial features, specific of how a person moves while talking, by means of metric learning coupled with an adversarial training strategy. The advantage is that we do not need any training data of fakes, but only train on real videos. Moreover, we utilize high-level semantic features, which enables robustness to widespread and disruptive forms of post-processing. We perform a thorough experimental analysis on several publicly available benchmarks. Compared to state of the art, our method improves generalization and is more robust to low-quality videos, that are usually spread over social networks. In particular, we obtain an average improvement of more than 15% in terms of accuracy for facial reenactment on high compressed videos.
This paper presents a method on workpiece detection based on imageprocessing and convolutional neural network(CNN). Firstly, four extreme points and center point of the workpiece are detected by imageprocessing tech...
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ISBN:
(数字)9781728192772
ISBN:
(纸本)9781728192789
This paper presents a method on workpiece detection based on imageprocessing and convolutional neural network(CNN). Firstly, four extreme points and center point of the workpiece are detected by imageprocessing technologies such as canny edge detection operator, morphological processing and denoising processing. And the predicted boxes fitting the shape of image is generated. Then, according to the coordinates of the extreme points, the image with a single workpiece is cut out, and a novel CNN named workpiece-net(wp-net) is created to classify the object. As a result, the accuracy of image cutting is 0.9986, the average Intersection over Union(IoU) is 0.9235; the parameter size of wp-net is 98.25K and the average precision of classification is 0.9883. In addition, the average recall of classification is 0.9877 and the speed of classification is 0.1243s/fps without Graphics processing Unit(GPU) and multithreading. Compared with the pure deep learning method, this method can detect more accurate coordinates which are consisted of extreme points. At the same time, the number of wp-net parameters and the complexity of the model structure used for classification are so far less than the popular deep neural network for detection that it can be easily deployed in embedded devices with limited storage space and computing power.
The paper considers the problem of multi-frame super-resolution under applicative noise which generates distributed regions of outlying observations (damaged regions) in initial low resolution images. It analyses the ...
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ISBN:
(数字)9781728188409
ISBN:
(纸本)9781728181134
The paper considers the problem of multi-frame super-resolution under applicative noise which generates distributed regions of outlying observations (damaged regions) in initial low resolution images. It analyses the existing solutions to the problem based on using recurrent algorithms of optimal filtering of a sequence of low resolution images. The paper also considers the existing super-resolution algorithms based on convolutional neural networks and deep learning models. A new approach and a corresponding imageprocessing scheme is suggested for multi-frame image super-resolution under applicative noise based on using a convolutional neural network. The paper presents the results of the experiment conducted in order to compare the considered approaches to multi-frame image super-resolution by means of a sequence of low resolution frames under applicative noise.
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