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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Min Liu Guangtao Zhai Zhenyu Zhang Yuntao Sun Ke Gu Xiaokang Yang Institute of Image Communication and Information Processing Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai China

image denoising has been fanatically researched for a very long time in that it is a commonplace yet important subject. The task to testify the performance of different image de-noising methods always resorts to PSNR in the past, until the emergence of SSIM, a landmark image quality assessment (IQA) metric. Since then, a vast majority of IQA methods were introduced in terms of various kinds of models. But unfortunately, those IQA metrics are along with more or less deficiencies such as the requirement of original images, making them far less than the ideal approaches. To address this problem, in this paper we propose an effective and blind image quality assessment for noise (dubbed BIQAN) to approximate the human visual perception to noise. The BIQAN is realized with three important portions, namely the free energy based brain principle, image gradient extraction, and texture masking. We conduct and compare the proposed BIQAN and a large amount of existing IQA metrics on three largest and most popular image quality databases (LIVE, TID2013, CSIQ). Results of experiments prove that the BIQAN has acquired very encouraging performance, outperforming those competitors stated above.

关键词： Measurement image quality Quality assessment Indexes PSNR

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Pathological Electrocardiogram Analysis based on Symbolic Relative Entropy

Pathological Electrocardiogram Analysis based on Symbolic Re...

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2013 International Conference on Bio-Medical Materials and Engineering(ICBME 2013)

作者： Jiakai Qiu Xiaoliang Huang Suyi Chang Yunxia Wang Jun Wang Image Processing and Image Communications Key Lab College of Geo & Bio InformationNanjing Univ of Posts & Telecomm

In this paper, symbolic relative entropy was used to analyze normal electrocardiogram(ECG), the ECG taken from patient with congestive heart failure(CHF) and Atrial fibrillation(AF) Statistical testing showed that the symbolic relative entropy of normal ECG was distinctly higher than that of CHF while the symbolic relative entropy of CHF was distinctly higher than that of AF It discoved that symbolic relative entropy can be used to analyze the different pathological ECG which could be used to assisted clinical diagnosis

关键词： Symbolic the Relative Entropy Electrocardiogram

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Transclaw U-Net: Claw U-Net With Transformers for Medical image Segmentation 5

Transclaw U-Net: Claw U-Net With Transformers for Medical Im...

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5th International Conference on Information communication and Signal processing, ICICSP 2022

作者： Yao, Chang Hu, Menghan Li, Qingli Zhai, Guangtao Zhang, Xiao-Ping East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing Shanghai China Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai China Ryerson University Department of Electrical Computer and Biomedical Engineering Toronto Canada

ISBN: (纸本)9781665485890

In medical image analysis, the long-range spatial features are often not accurately obtained by the traditional convolutional neural networks. Hence, we propose a TransClaw U-Net network structure. The transformer part is added after three convolution operations to fuse shallow features extracted by convolution operations for maximally encoding the long-range spatial features between patches. The 'Claw' in TransClaw U-net means that we add the bottom upsampling part to retain the deepest feature information for detail segmentation. In addition, the modified three-channel global attention mechanism to blend the outputs of three channels (the encoding part, the bottom upsampling part and the decoding part) to effectively extract image contours. The experimental results on Synapse Multi-organ Segmentation Dataset show that TransClaw U-Net performs better than other networks. The results of ablation experiments prove the effectiveness of the three improved components of the network and influence of input image size and skip-connection numbers on network performance. The source code will be publicly available once the paper is accepted. © 2022 IEEE.

关键词： Convolution

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The simulation models for rayleigh fading channels

The simulation models for rayleigh fading channels

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2007 2nd International Conference on communications and Networking in China, ChinaCom 2007

作者： Zhinian, Luo Wenjun, Zhang Institute of Image Communication and Information Processing Department of Electronic Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Computer and Communication Hunan University ChangSha 410082 China Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781424410095

Rayleigh fading channel is the foundation of mobile radio channel modeling. This paper summarizes four classes of the simulation models for Rayleigh fading channels based on sum-of-sinusoids by a uniform expression, according to the different definition of the assumed parameters in the expression. Among these parameters, the initial phases should be set as random variables and we conclude three cases of phases' definition. With the help of these discussions, it can be seen that the ergodicity and the stationary can not exist simultaneously when the number of multipath wave is finite. Based on this conclusion, some conditions on an effective channel model are proposed, and these conclusions are useful to model the new effective mobile radio channels.

关键词： Fading channels

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A compressive sensing based mobile video communication system

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Sensors and Transducers 2013年第6期153卷 186-191页

作者： Wu, Minghu Li, Ran Zhou, Shangli School of Electrical and Electronic Engineering Hubei University of Technology Wu'han 430068 China Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing 210003 China

A compressive sensing (CS) based mobile video communication system is proposed to meet the requirement that video service needs low-complexity video encoder in the mobile internet. In this system, the mobile client uses CS based video encoder having low computational complexity and power, and then its output bit-stream is decoded using CS recovery algorithm in the fixed base station or network server. The decoded video signal is transformed again as bits by the traditional H.264/MPEG video encoder, and the central base station transmits them to mobile clients so as to realizing low-complexity video decoding. The simulation experiments show that the proposed CS based mobile video communication system has the better rate-distortion performance, and can ensure high quality video reconstruction at low measurement rates. © 2013 IFSA.

关键词： Compressed sensing

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Separation of superimposed images with unknown motions using sparsity priors

Separation of superimposed images with unknown motions using...

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IEEE Visual communications and image processing (VCIP)

作者： Qing Yan Xiaokang Yang Yi Xu The Institute of Image Communication and Information Processing Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai China

When people take photos through a transparent surface, it is ubiquitous that the images are superimposed with two source images, the transmitted layer and the reflected layer. In this paper, we utilize the sparsity priors over image color distribution and image gradients, and we realize the separation of such images by exploiting the diversity (relative motions of source images) in different snapshots. Our approach can estimate the motion even when layer image is quite faint, extract layer gradients when layer intensity is unchanged, and obtain a reliable separation by considering the gradients as limitations. The effectiveness of our approach is shown in experiments on both synthetic images and real world photos.

关键词： image color analysis Feature extraction Transforms Transmission line matrix methods Reliability PSNR Surface morphology

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Simultaneous triple subtitles exhibition via Temporal Psychovisual Modulation

Simultaneous triple subtitles exhibition via Temporal Psycho...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Chunjia Hu Guangtao Zhai Zhongpai Gao Xiongkuo Min Institute of Image Communication and Information Processing Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai China

ISBN: (纸本)9781479943142

With the quick acceleration of cultural exchange and globalization, multi-language annotation for in-theatre movie exhibition is gaining more attention. The existing solution overlays a stack of subtitles in different languages over the movie, for example, the familiar combination of English + local tongue as seen in the cinema today. However, a major drawback of this straightforward piling-up type of solution is that as the required number of languages increases, the subtitle area grows and it may eventually span over the movie contents. An effective multi-language subtitling system without compromising the movie watching experience is therefore highly desired. In this paper, we propose a new Temporal Psychovisual Modulation (TPVM) based solution to the multi-language subtitling problem. TPVM is a new paradigm of information display exploiting the mismatch between high refresh rate of the modern optoelectronic displays and limited temporal resolution of the human visual system (HVS). In this work, we design a simultaneous triple subtitle exhibition system using 2 120 Hz stereoscopic DLP projectors with linear polarization filters and LCD shutter glasses. Wearing different pairs of glasses, the audience can enjoy the movie with 3 optional subtitles without interfering with the audience without the glasses and see the movie directly. Extended experimental results will be given in this paper to justify the effectiveness and robustness of the proposed multiple subtitling system.

关键词： Glass Motion pictures Modulation Stereo image processing Synchronization Visual systems Liquid crystal displays

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Foreground estimation based on robust linear regression model

Foreground estimation based on robust linear regression mode...

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IEEE International Conference on image processing

作者： Gengjian Xue Li Song Jun Sun Meng Wu Institute of Image Communication and Information Processing Shanghai Key Laboratories of Digital Media Processing and Communication Shanghai Jiaotong University Shanghai China

Background subtraction is a basic task for many computer vision applications, yet in dynamic scenes it is still a challenging problem. In this paper, we propose a new method to deal with this difficulty. Our approach is based on robust linear regression model and casts background subtraction as a outlier signal estimation problem. In our linear regression model, we explicitly model the error term as a combination of two components: foreground outlier and background noise. The foreground outlier is sparse and can be arbitrarily large in most cases, while the background noise is relatively small and dispersed. In order to reliably estimate the coefficients under the constraint of sparse foreground outlier, we propose a new objective function. Then we transform the function to fit our problem by only estimating the foreground outlier and give the solution method. Experimental results demonstrate the effectiveness of our method.

关键词： Linear regression Estimation Robustness Noise measurement image processing Mathematical model Conferences

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Selective Bokeh Effect Transformation

Selective Bokeh Effect Transformation

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Peng, Juewen Pan, Zhiyu Liu, Chengxin Luo, Xianrui Sun, Huiqiang Shen, Liao Xian, Ke Cao, Zhiguo Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control China Nanyang Technological University S-Lab Singapore

ISBN: (纸本)9798350302493

Bokeh effect transformation is a novel task in computer vision and computational photography. It aims to convert bokeh effects from one camera lens to another. To this end, we introduce a new concept of blur ratio, which represents the ratio of the blur amount of a target image to that of a source image, and propose a novel framework SBTNet based on this concept. For cat-eye simulation and lens type transformation, a two-channel coordinate map and a two-channel one-hot map are added as extra inputs. The core of the framework is a sequence of parallel FeaNets, along with a feature selection and integration strategy, which aims to transform the blur amount with arbitrary blur ratio. The effectiveness of the proposed framework is demonstrated through extensive experiments, and our solution has achieved the top LPIPS metric in NTIRE 2023 Bokeh Effect Transformation Challenge. © 2023 IEEE.

关键词： Color photography

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Hybrid depression classification and estimation from audio video and text information 7

Hybrid depression classification and estimation from audio v...

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7th Annual Workshop on Audio/Visual Emotion Challenge, AVEC 2017

作者： Yang, Le Pei, Ercheng Sahli, Hichem Oveneke, Meshia Cédric Xia, Xiaohan Jiang, Dongmei Shaanxi Key Lab on Speech and Image Information Processing 127 Youyi Xilu Xi'an710072 China Pleinlaan 2 Brussels1050 Belgium Kepeldreef 75 Heverlee3001 Belgium

ISBN: (纸本)9781450355025

In this paper, we design a hybrid depression classification and depression estimation framework from audio, video and text descriptors. It contains three main components: 1) Deep Convolutional Neural Network (DCNN) and Deep Neural Network (DNN) based audio visual multi-modal depression recognition frameworks, trained with depressed and not-depressed participants, respectively;2) Paragraph Vector (PV), Support Vector Machine (SVM) and Random Forest based depression classification framework from the interview transcripts;3) A multivariate regression model fusing the audio visual PHQ-8 estimations from the depressed and notdepressed DCNN-DNN models, and the depression classification result from the text information. In the DCNN-DNN based depression estimation framework, audio/video feature descriptors are first input into a DCNN to learn high-level features, which are then fed to a DNN to predict the PHQ-8 score. Initial predictions from the two modalities are fused via a DNN model. In the PV-SVM and Random Forest based depression classification framework, we explore semantic-related text features using PV, as well as global text-features. Experiments have been carried out on the Distress Analysis Interview Corpus - Wizard of Oz (DAIC-WOZ) dataset for the Depression Sub-challenge at the 2017 Audio-Visual Emotion Challenge (AVEC), results show that the proposed depression recognition framework obtains very promising results, with root mean square error (RMSE) as 3.088, mean absolute error (MAE) as 2.477 on the development set, and RMSE as 5.400, MAE as 4.359 on the test set, which are all lower than the baseline results. ©2017 Association for Computing Machinery.

关键词： Support vector machines

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