Perivascular spaces are fluid-filled tubular spaces that follow the course of cerebral penetrating vessels, thought to be a key part in the brain's circulation and glymphatic drainage system. Their enlargement and...
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ISBN:
(纸本)9783030527914;9783030527907
Perivascular spaces are fluid-filled tubular spaces that follow the course of cerebral penetrating vessels, thought to be a key part in the brain's circulation and glymphatic drainage system. Their enlargement and abundance have been found associated with cerebral small vessel disease. Thus, their quantification is essential for establishing their relationship with neurological diseases. Previous works in the field have designed visual rating scales for assessing the presence of perivascular spaces and proposed segmentation techniques to reduce flooring and ceiling effects of qualitative visual scales, processing times, and inter-observer variability. Nonetheless, their application depends on the acquisition quality. In this paper, we propose a framework for improving perivascular spaces quantification using both texture analysis and total variation filtering. Texture features were considered for evaluating the image quality and determining automatically whether filtering was needed. We tested our work using data from a cohort of patients with mild stroke (n = 60) with different extents of small vessel disease features and image quality. Our results demonstrate the potential of our proposal for improving perivascular spaces assessments.
In this work, a new objective image quality assessment (IQA) framework, based on the working principle of permutation entropy (PE) is proposed. The framework is titled as Permutation Entropy Deviation Index (PEDI). Th...
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Reflection removal is a long-standing problem in computer vision. In this paper, we consider the reflection removal problem for stereoscopic images. By exploiting the depth information of stereoscopic images, a new ba...
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ISBN:
(数字)9781728180687
ISBN:
(纸本)9781728180694
Reflection removal is a long-standing problem in computer vision. In this paper, we consider the reflection removal problem for stereoscopic images. By exploiting the depth information of stereoscopic images, a new background edge estimation algorithm based on the Wasserstein Generative Adversarial Network (WGAN) is proposed to distinguish the edges of the background image from the reflection. The background edges are then used to reconstruct the background image. We compare the proposed approach with the state-of-the- art reflection removal methods. Results show that the proposed approach can outperform the traditional single-image based methods and is comparable to the multiple-image based approach while having a much simpler imaging hardware requirement.
This paper proposes a novel image steganography algorithm for color image. Recently, colorization-based image coding technique has been studied. In order to compress the color image effectively, this technique transfo...
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With the development of deep learning, many methods on image denoising have been proposed processingimages on a fixed scale or multi-scale which is usually implemented by convolution or deconvolution. However, excess...
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ISBN:
(数字)9781728180687
ISBN:
(纸本)9781728180694
With the development of deep learning, many methods on image denoising have been proposed processingimages on a fixed scale or multi-scale which is usually implemented by convolution or deconvolution. However, excessive scaling may lose image detail information, and the deeper the convolutional network the easier to lose network gradient. Diamond Denoising Network (DmDN) is proposed in this paper, which mainly based on a fixed scale and meanwhile considering the multi-scale feature information by using the Diamond-Shaped (DS) module to deal with the problems above. Experimental results show that DmDN is effective in image denoising.
A simple and effective low-light image enhancement method based on a noise-aware texture-preserving retinex model is proposed in this work. The new method, called NATLE, attempts to strike a balance between noise remo...
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ISBN:
(数字)9781728180687
ISBN:
(纸本)9781728180694
A simple and effective low-light image enhancement method based on a noise-aware texture-preserving retinex model is proposed in this work. The new method, called NATLE, attempts to strike a balance between noise removal and natural texture preservation through a lowcomplexity solution. Its cost function includes an estimated piece-wise smooth illumination map and a noise-free texture-preserving reflectance map. Afterwards, illumination is adjusted to form the enhanced image together with the reflectance map. Extensive experiments are conducted on common low-light image enhancement datasets to demonstrate the superior performance of NATLE.
The proceedings contain 376 papers. The special focus in this conference is on Neural Information processing. The topics include: DF-PLSTM-FCN: A Method for Unmanned Driving Based on Dual-Fusions and Parallel LSTM-FCN...
ISBN:
(纸本)9783030638221
The proceedings contain 376 papers. The special focus in this conference is on Neural Information processing. The topics include: DF-PLSTM-FCN: A Method for Unmanned Driving Based on Dual-Fusions and Parallel LSTM-FCN;learning Discrete Sentence Representations via Construction & Decomposition;sparse Hierarchical Modeling of Deep Contextual Attention for Document-Level Neural Machine Translation;improving Mongolian-Chinese Machine Translation with Automatic Post-editing;exploration on the Generation of Chinese Palindrome Poetry;error Heuristic Based Text-Only Error Correction Method for Automatic Speech Recognition;detecting Online Fake Reviews via Hierarchical Neural Networks and Multivariate Features;deep Cardiovascular Disease Prediction with Risk Factors Powered Bi-attention;a Hybrid Self-Attention Model for Pedestrians Detection;coarse-to-Fine Attention Network via Opinion Approximate Representation for Aspect-Level Sentiment Classification;CARU: A Content-Adaptive Recurrent Unit for the Transition of Hidden State in NLP;automatic Parameter Selection of Granual Self-organizing Map for Microblog Summarization;A Token-Wise CNN-Based Method for Sentence Compression;a Neural Framework for English-Hindi Cross-Lingual Natural Language Inference;WC2FEst-Net: Wavelet-Based Coarse-to-Fine Head Pose Estimation from a Single image;unsupervised Tongue Segmentation Using Reference Labels;u-Net Neural Network Optimization Method Based on Deconvolution Algorithm;a Feature Fusion Network for Multi-modal Mesoscale Eddy Detection;triple Attention Network for Clothing Parsing;temporal Smoothing for 3D Human Pose Estimation and Localization for Occluded People;Take a NAP: Non-Autoregressive Prediction for Pedestrian Trajectories;simultaneous Inpainting and Colorization via Tensor Completion;REXUP: I REason, I EXtract, I UPdate with Structured Compositional Reasoning for visual Question Answering;res2U-Net: image Inpainting via Multi-scale Backbone and Channel Attention;drawing Drea
The proceedings contain 376 papers. The special focus in this conference is on Neural Information processing. The topics include: DF-PLSTM-FCN: A Method for Unmanned Driving Based on Dual-Fusions and Parallel LSTM-FCN...
ISBN:
(纸本)9783030638191
The proceedings contain 376 papers. The special focus in this conference is on Neural Information processing. The topics include: DF-PLSTM-FCN: A Method for Unmanned Driving Based on Dual-Fusions and Parallel LSTM-FCN;learning Discrete Sentence Representations via Construction & Decomposition;sparse Hierarchical Modeling of Deep Contextual Attention for Document-Level Neural Machine Translation;improving Mongolian-Chinese Machine Translation with Automatic Post-editing;exploration on the Generation of Chinese Palindrome Poetry;error Heuristic Based Text-Only Error Correction Method for Automatic Speech Recognition;detecting Online Fake Reviews via Hierarchical Neural Networks and Multivariate Features;deep Cardiovascular Disease Prediction with Risk Factors Powered Bi-attention;a Hybrid Self-Attention Model for Pedestrians Detection;coarse-to-Fine Attention Network via Opinion Approximate Representation for Aspect-Level Sentiment Classification;CARU: A Content-Adaptive Recurrent Unit for the Transition of Hidden State in NLP;automatic Parameter Selection of Granual Self-organizing Map for Microblog Summarization;A Token-Wise CNN-Based Method for Sentence Compression;a Neural Framework for English-Hindi Cross-Lingual Natural Language Inference;WC2FEst-Net: Wavelet-Based Coarse-to-Fine Head Pose Estimation from a Single image;unsupervised Tongue Segmentation Using Reference Labels;u-Net Neural Network Optimization Method Based on Deconvolution Algorithm;a Feature Fusion Network for Multi-modal Mesoscale Eddy Detection;triple Attention Network for Clothing Parsing;temporal Smoothing for 3D Human Pose Estimation and Localization for Occluded People;Take a NAP: Non-Autoregressive Prediction for Pedestrian Trajectories;simultaneous Inpainting and Colorization via Tensor Completion;REXUP: I REason, I EXtract, I UPdate with Structured Compositional Reasoning for visual Question Answering;res2U-Net: image Inpainting via Multi-scale Backbone and Channel Attention;drawing Drea
With the explosive increase of image data, the efficiency of both image compression and retrieval becomes unprecedentedly significant. However, these two tasks are usually isolated executed, which waste great computat...
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In the modern world, image transmission, including transmission of television and video signals, is carried out using a preliminary and restorative correction according to the law, which was due to the use of imaging ...
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ISBN:
(纸本)9781728106069
In the modern world, image transmission, including transmission of television and video signals, is carried out using a preliminary and restorative correction according to the law, which was due to the use of imaging devices on cathode ray tubes. This law differs from the law of dependence of the visibility threshold of a visual stimulus on its magnitude, but continues to be used for compatibility reasons. Therefore, in the work, instead of linearizing the sensations of the quantization scale of a television signal, it is proposed to optimize the choice of code combinations for image transmission. This approach can be effective in television systems without compression of video information with losses, for example, for transmitting signals over short distances or in channels for transmitting signals of the image of Earth remote sensing systems
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