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Multi-image Reconstruction in Multi-Contrast MRI 29

Multi-Image Reconstruction in Multi-Contrast MRI

引用

29th IEEE conference on Signal processing and communications Applications (SIU)

作者： Ozbey, Muzaffer Cukur, Tolga Bilkent Univ Elekt & Elekt Muhendisligi Bolumu Ankara Turkey Bilkent Univ Ulusal Manyetik Rezonans Arastirma Merkezi Ankara Turkey Bilkent Univ Sinirbilim Programi Muhendislik & Fen Bilimleri Enstitusu Ankara Turkey

ISBN: (纸本)9781665436496

The acquisition of multiple contrast magnetic resonance images (MRI) has an important role in clinical diagnosis by increasing diagnostic knowledge. Long scanning durations, in which the patient must remain immobile, limits the acquisition of multiple contrast MRIs. Scanning times can be reduced by undersampled acquisitions and reconstruction of undersampled images. Common methods produce a fully sampled MR image of a single contrast from undersampled MR image of the same contrast. However, limited prior information of a single contrast MR image in the input data limits the reconstruction performance. Hence, reconstruction performance can be increased with the use of multiple contrast MRI as input data. In this study, a multi-contrast MRI reconstruction method is proposed which simultaneously produces fully sampled images from undersampled images of more than one contrast. This method is applied using generative adversarial network that produce highly realistic images by better recovering the high frequency details. The proposed method has been tested on a dataset containing multiple contrast brain MR images, and it has been demonstrated that it provides superior performance compared to the alternative single contrast reconstruction method as a result of numerical and visual evaluations.

关键词： MR image reconstruction Deep learning Generative adversarial network Multi-contrast MRI

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Research on Single image Deraining Algorithm Based on Euclidean Distance with Adaptive Progressive Residual Network 23

Research on Single Image Deraining Algorithm Based on Euclid...

引用

23rd IEEE International conference on High Performance Computing and communications, 7th IEEE International conference on Data Science and Systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Xingzhi Ma, Ruiqiang Dong, Zehui Inner Mongolia University of Technology School of Data Science and Application Inner Mingolia China

ISBN: (纸本)9781665494571

In rainy days, rain streaks have various directions, shapes and densities. In order to achieve the purpose of rain removal, the deep network structure becomes complicated, thus reducing the efficiency of rain removal. The same model have an incomplete rain streaks removal problem for various rainy images. This paper proposes the adaptive progressive residual network (APRN) to solve the above problems. APRN used recursive method to achieve progressive rain streaks removal. We used the Euclidean distance to determine the distance between the derain images, and designed the Euclidean distance ratio (EDR) threshold to stop the network recursion and output final derain image. EDR compares the Euclidean distance of the de-rain images in adjacent stages, and controls the network recursion and helps the network processes rainy images more efficiently. Experiments show that APRN has better visual effects on different datasets, and the processing efficiency is improved. © 2021 IEEE.

关键词： Rain Adaptive systems Target tracking Shape Semantics Process control Euclidean distance

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FCADD: Robust Watermarking Resisting JPEG Compression with Frequency Channel Attention and Distortion De-gradient

FCADD: Robust Watermarking Resisting JPEG Compression with F...

引用

IEEE International conference on Trust, Security and Privacy in Computing and communications (TrustCom)

作者： Dong Yang Weihai Li Zikai Xu Zhiling Zhang Yiling Chen Anhui Province Key Laboratory of Digital Security School of Cyber Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Recently, the majority of deep learning-based robust watermarking techniques exhibit proficiency in concealing secret information, thereby providing effective protection for the copyright information of images. However, during the digital transmission process, images typically undergo JPEG compression, which significantly compromises the integrity of the watermarks. We found that most current methodologies are inadequately equipped to mitigate the adverse impacts of JPEG compression. In this work, we proposed a robust watermarking method against JPEG compression by applying Frequency Channel Attention and Distortion De-gradient mechanism (FCADD). Specifically, we introduce a only forward propagation mechanism in the attack simulation layer that bypasses the difference caused by image processing, such as compression, dropout, during transmission, allowing a portion of the loss function’s gradient to be passed back to the encoder. Additionally, for the sake of model stability and generalizability, we assign a certain probability to both the JPEG simulation function and a lossless function in different training batches to participate in the training process. Furthermore, we introduce Frequency Channel Attention Blocks (FCA Block) in the feature extraction process, leveraging the information on the image frequency domain to achieve better visual effects. Extensive experiments have demonstrated that our approach outperforms the current state-of-the-art (SOTA) models in JPEG compression across any quality factor, and also exhibits superior visual effects and robustness against other distortions such as Gaussian filtering and Dropout.

关键词： Training Q-factor image coding Frequency-domain analysis Transform coding Watermarking Distortion visual effects Robustness Stability analysis

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End-to-End image Compression via Attention-Guided Information-Preserving Module

End-to-End Image Compression via Attention-Guided Informatio...

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Duan, Wenhong Lin, Kai Jia, Chuanmin Zhang, Xinfeng Ma, Siwei Gao, Wen Shanghai Jiao Tong University Shanghai China Peking University National Engineering Research Center of Visual Technology School of Computer Science Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Deep learning-based end-to-end image compression has achieved significant compression performance in recent years. However, current learning-based image compression methods are designed considering the characteristic of RGB color space, which is not suitable for image compression in YUV 420 color space because of the variance between color formats. To achieve efficient image compression in YUV 420 color space, we propose an information-preserving compression framework using the attention mechanism. Specifically, we design an information-preserving module (IPM), where we utilize the dual-branch architecture to prevent changes in data distribution and propose the feature attention block (FAB) to preserve information. Furthermore, a cross-channel progressive enhancement (CPE) network is designed by taking advantage of the relations among different channels. Ex-perimental results show that the proposed framework outper-forms state-of-the-art compression standard Versatile Video Coding (VVC) with 2.52% BD-rate reduction on common test conditions (CTC) sequences on average. © 2022 IEEE.

关键词： image compression

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A Progressive and Multi-Prior-Guided Network for image Inpainting

A Progressive and Multi-Prior-Guided Network for Image Inpai...

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Peng, Bo Qiu, Yining Wu, Dong He, Liren Chi, Mingmin School of Computer Science Fudan University Shanghai Key Laboratory of Data Science Shanghai China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Deep learning techniques have recently made considerable progress in image inpainting by introducing prior knowledge, e.g. texture and structure. However, the existing methods still suffer from artefacts such as distorted texture and abrupt colors due to insufficient consideration of correlation between the prior visual features. In this paper, we propose a novel progressive and multi-prior-guided network (PMPN) for image inpainting inspired by the human painting process, which first constructs the sketch of painting art, then generates the corresponding textures finally fills the colors to the appropriate locations. In particular, to model the global multi-scale contexts during the reconstruction process, we design a bi-directional cross-stage perception module that captures spatial information across branches and stages and guides the model to synthesize a natural and consistent texture. Our proposed PMPN network is evaluated on three publicly available datasets, outperforming the current state-of-the-art models. © 2022 IEEE.

关键词： Computer vision

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Indoor visible light positioning algorithm based on point and line feature visual information in occluding environment

Indoor visible light positioning algorithm based on point an...

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Networks, communications and Intelligent Computing (NCIC), International conference on

作者： Xiangyun Ji Xu Bao School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China

ISBN: (数字)9798350371949

ISBN: (纸本)9798350371956

This paper proposes a method to restore the pixel coordinate points under different occlusions of rectangular lights, which can solve the problem of capturing incomplete light emitting diode(LED) images and unable to deal with positioning. The experimental results show that compared with the unoccluded situation, the positioning accuracy under general occlusion can reach an average positioning error of 2.09cm, and the positioning accuracy under severe occlusion can reach an average positioning error of 2.43cm. Noise in visual positioning affects the positioning results, algorithm processing and mathematical modeling can optimize the positioning accuracy, reducing the influence of positioning error caused by image noise by 60%.

关键词： visualization Noise Light emitting diodes Mathematical models Data models image restoration

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MAiVAR: Multimodal Audio-image and Video Action Recognizer

MAiVAR: Multimodal Audio-Image and Video Action Recognizer

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IEEE visual communications and image processing (VCIP)

作者： Muhammad Bilal Shaikh Douglas Chai Syed Mohammed Shamsul Islam Naveed Akhtar Edith Cowan University Perth WA Australia The University of Western Australia Perth WA Australia

ISBN: (纸本)9781665475938

Currently, action recognition is predominately performed on video data as processed by CNNs. We investigate if the representation process of CNN s can also be leveraged for multimodal action recognition by incorporating image-based audio representations of actions in a task. To this end, we propose Multimodal Audio-image and Video Action Recognizer (MAiVAR), a CNN-based audio-image to video fusion model that accounts for video and audio modalities to achieve superior action recognition performance. MAiVAR extracts meaningful image representations of audio and fuses it with video representation to achieve better performance as compared to both modalities individually on a large-scale action recognition dataset.

关键词： image segmentation image recognition visual communication Fuses image representation Feature extraction Kinetic theory

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Partial Inter-Frame Coding for Dynamic Meshes

Partial Inter-Frame Coding for Dynamic Meshes

引用

IEEE International conference on image processing

作者： Xudong Jin Jianfeng Xu Kei Kawamura Advanced Visual Communication Laboratory XR Division KDDI Research Inc.

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The MPEG-3DGH group is advancing a new standard for dynamic mesh encoding, known as Video-based Dynamic Mesh Coding (V-DMC). While V-DMC improves efficiency by toggling between intra- and inter-frame coding, it struggles with dynamic meshes that change inconsistently across frames. To address this issue, we introduce a novel partial inter-frame coding mode within the V-DMC framework. The method initiates by partitioning the adjacent frames into temporally aligned segments. These segments are then classified as suitable for either intra-frame or inter-frame coding based on the detection of inconsistent changes. Leveraging these segmentations, we introduce a partial inter-frame coding mode that allows for the selective application of intra- and inter-frame coding at the level of individual segments. Experiments on the MPEG V-DMC test sequences demonstrate that our method can achieve BD-rate improvement over the anchor in terms of D1, D2, Luma, Cb, and Cr PSNRs by -2.1%,-2.7%,-4.7%,-5.4%, and -4.5%, respectively.

关键词： image segmentation image coding Transform coding Streaming media Aerodynamics Distortion Complexity theory

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image Denoising Using a Deep Auto-encoder Approach Based on Beetle Antennae Search Algorithm 1

引用

2nd International conference on Computer and Communication Engineering, CCCE 2022

作者： Xiang, Qian Zhu, Peng College of Information Science and Engineering Wuchang Shouyi University Wuhan China

ISBN: (数字)9783031174223

ISBN: (纸本)9783031174216

image will be polluted by different kinds of noise in the process of acquisition, compression, and transmission, resulting in interference to subsequent image segmentation, feature extraction and other processing. With the development of deep convolutional neural network (DCNN), quite a few effective DCNNs have been designed and have made remarkable progress in image denoising. Gradient descent algorithm is generally used for DCNN training. However, due to the complex mathematical properties of the high-dimensional and non-convex loss optimization surface, there are often many local optimal points, saddle points or large range of gradient gentle regions, which affects training effect of the gradient descent algorithm. Although intelligent algorithms such as evolutionary algorithm have global optimization capability, they often have large computing resource requirements and slow convergence speed, which limit its application in DCNN training which is a high-dimensional optimization problem. Beetle antennae search (BAS) algorithm is a simple and efficient bionic intelligent optimization algorithm, which has global search ability. In this paper, the gradient descent method and BAS method are combined as a hybrid method for deep auto-encoder (DAE) denoising network training. Experimental results show that the proposed method accelerates the training speed of the DAE denoising network, reduces the blurring of edge details and improves the visual effect of the restored image. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image enhancement

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Adaptive Multi-Feature Extraction Graph Convolutional Networks for Multimodal Target Sentiment Analysis

Adaptive Multi-Feature Extraction Graph Convolutional Networ...

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Xiao, Luwei Zhou, Ejian Wu, Xingjiao Yang, Shuwen Ma, Tianlong He, Liang School of Computer Science and Technology East China Normal University Shanghai China East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing Shanghai China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

The multi-modal target-oriented sentiment analysis aims at predicting the sentiment polarities for target entities in a sentence by combining vision and language information. However, most existing deep learning approaches fail to extract valuable information from the visual modality and ignore the usability of syntactic dependency information embedded in the text modality. In this paper, we propose a two-stream adaptive multi-feature extraction graph convolutional networks (AME-GCN), which translates the image into a textual caption and dynamically fuses the semantic and syntactic feature from the given sentence and generated caption to model the inter/intra-modality dynamics. Extensive experiments on two multi-modal Twitter datasets show the effectiveness of the proposed model against popular textual and multi-modal approaches, demonstrating that AME-GCN is a best alternative for this task. © 2022 IEEE.

关键词： Sentiment analysis

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