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Video Compression Artifacts Removal With Spatial-Temporal Attention-Guided Enhancement

IEEE Transactions on Multimedia 2024年 26卷 5657-5669页

作者： Jiang, Nanfeng Chen, Weiling Lin, Jielian Zhao, Tiesong Lin, Chia-Wen Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou350116 China Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Recently, many compression algorithms are applied to decrease the cost of video storage and transmission. This will introduce undesirable artifacts, which severely degrade visual quality. Therefore, Video Compression Artifacts Removal (VCAR) aims at reconstructing a high-quality video from its corrupted version of compression. Generally, this task is considered as a vision-related instead of media-related problem. In vision-related research, the visual quality has been significantly improved while the computational complexity and bitrate issues are less considered. In this work, we review the performance constraints of video coding and transfer to evaluate the VCAR outputs. Based on the analyses, we propose a Spatial-Temporal Attention-Guided Enhancement Network (STAGE-Net). First, we employ dynamic filter processing, instead of conventional optical flow method, to reduce the computational cost of VCAR. Second, we introduce self-attention mechanism to design Sequential Residual Attention Blocks (SRABs) to improve visual quality of enhanced video frames with bitrate constraints. Both quantitative and qualitative experimental results have demonstrated the superiority of our proposed method, which achieves high visual qualities and low computational costs. © 2023 IEEE.

关键词： Computational complexity

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Abundant Semantic Information Module for Object Detection 2

Abundant Semantic Information Module for Object Detection

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2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： Wu, Taida Li, Qishen Li, Qiufeng Gao, Jun School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China Jiangxi Nanchang China

ISBN: (纸本)9798350334784

Nowadays, with the high-speed iteration of convolution neural network, the efficient object detector emerges one after another. As an important branch of computer vision, object detection aims to detect where and what the object is. However, nowadays, many detector cannot extract abundant semantic information to discriminate the location and size of the objects, resulting in poor performance of the network. In this paper, a new module is proposed, named Abundant Semantic Information Module (ASIM), to enrich and expand the semantic information of the object with more and larger receptive fields. In ASIM, we blend the extracted feature maps to different degrees with different blending factors and fuse them so that all object information is given full attention. Compared to the baseline method, a wealth of experiments show that our module has achieved a significant performance improvement. © 2022 IEEE.

关键词： Object detection

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Complexity optimization of adaptive RBF networks

Complexity optimization of adaptive RBF networks

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13th International Conference on pattern recognition, ICPR 1996

作者： Leonardis, Aleš Bischof, Horst Department for Pattern Recognition and Image Processing Technical University Vienna Treitlstraße 3/1832 A -1040 Vienna Austria University of Ljubljana Faculty of Computer and Information Science Computer Vision Laboratory Ljubljana Slovenia

ISBN: (纸本)081867282X

We propose an extension of RBF networks which includes a mechanism for optimizing the complexity of the network. The approach involves two procedures: adaptation (training) and selection. The first procedure adaptively changes the locations and the width of the centers of the basis functions and trains the linear weights. The selection procedure performs the elimination of some of the basis functions using an objective function. By iteratively combining these two procedures we achieve a controlled way of training and modifying RBF networks, which balances accuracy, learning time, and complexity of the resulting network. © 1996 IEEE.

关键词： Radial basis function networks

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Efficient Texture Classification using Short-time Fourier Transform with Spatial Pyramid Matching

Efficient Texture Classification using Short-time Fourier Tr...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Hassan Dawood Hussain Dawood Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)9781479906505

Texture feature extraction plays an important role in texture image classification. In this paper, we have proposed a texture feature extraction method by utilizing the Short-time Fourier Transform to provide local image information, and for the global geometric correspondence we have proposed to use Spatial Pyramid Matching in frequency domain named as Short-time Fourier Transform with Spatial Pyramid Matching (STFT-SPM). The experiments are conducted on standard benchmark datasets for texture classification like Brodatz and KTH-TIPS2-a, shows that STFT-SPM can achieve significant improvement compared to the Local Phase Quantization, Weber local Descriptor and local Binary pattern methods.

关键词： Spatial Pyramid Matching Texture classification Short-time Fourier Transform STFT Fourier Transform Pyramid texture image information textures standardized benchmark Feature extraction

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JOINT BLURRED image RESTORATION WITH PARTIALLY KNOWN INFORMATION

JOINT BLURRED IMAGE RESTORATION WITH PARTIALLY KNOWN INFORMA...

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： QING WU XING-CE WANG PING GUO Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

A new restoration method for joint blurred images with partially known information is proposed in this paper. The joint blur here is assumed to be motion blurs and defocus blur mixed together. Under the condition of two blur effects are supposed to be independent linear shift-invariant processes and motion blur parameter can be obtained with known information, a reduced update Kalman filter (RUKF) is used for degraded image restoration and the best defocus point spread function (PSF) parameter is determined based on the maximum entropy principle (MEP). Experimental results with real images show that the proposed approach works well.

关键词： PSF estimation Joint blurred image Reduced update Kalman filter Maximum entropy principle

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Second order morphology algorithm for IR small target enhancement in sea clutter background

Second order morphology algorithm for IR small target enhanc...

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2010 International Conference on Optics, Photonics and Energy Engineering, OPEE 2010

作者： Xiao, Zhou Xingang, Mou Guilin, Zhang Natl. Key Laboratory of Science and Technology on Multi-spectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781424452354

The framework of a second order morphology algorithm is proposed to enhance the dim small infrared target in sea clutter background with strong detector noise. First, a morphological filters bank is given. Each filter in the bank is designed to suppress its corresponding type texture. The second order process can be accomplished in two steps. The original infrared image is first filtered using each filter separately, and then the first order result is obtained by the combination of the filtered result images. The other step is essentially the same as the first one except the combination manner of the filtered result images. Second, the traditional SNR gain based evaluation of different small target enhancement algorithms is examined and a more reasonable method based on the distance between the target and the maximal background (DTMB) is described in this paper. Third, using the new evaluation method, the comparison is given by experiment through a sequence of infrared images with a dim small target. Experimental results demonstrate that the new proposed algorithm performs better in sea clutter background with strong detector noise than the traditional ones. © 2010 IEEE.

关键词： Morphology

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Two-dimensional Spectral image Calibration Based on Feed-forward Neural Network

Two-dimensional Spectral Image Calibration Based on Feed-for...

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International Joint Conference on Neural Networks

作者： Mingze Li Hasitieer Haerken Ping Guo Fuqing Duan Qian Yin Xin Zheng Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)9781509061839

In this paper, we present a novel method on image calibration, utilizing Total Least Square (TLS) method and Feed-forward Neural Network, to solve the aberration problem of LAMOST two-dimensional astronomical spectral images. In our method, training sample set is generated with domain knowledge, from which a number of discrete points are are extracted from spectral images with fiber tracing method, and output vectors are formed by the corresponding calibrated points, obtained by utilizing the TLS method. The Feed-forward Neural Network is trained to obtain the transformation matrix, casting about for the matching relationship between the input and output sets. We also perform comparative experiments on fiber tracing and spectrum extraction results between calibrated spectral images and uncalibrated spectral images, the results show an advantage of higher accuracy and precision by our proposed method.

关键词： Nonlinear distortion Calibration Neural networks Training Optical distortion Imaging

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On realization of visualization system for global ocean simulation

On realization of visualization system for global ocean simu...

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International Conference on Audio, Language and image processing

作者： Ming, De-Lie Gao, Peng-Fei Wang, Mi State Key Laboratory for Multi-spectral Information Processing Technology Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan Hubei Province 430074 China

ISBN: (纸本)9781424458653

Simulating and rendering realistic ocean is always one of the most popular and difficult tasks in computer graphics and oceanography. However, because of the restriction of computer software and hardware conditions, mass data and real-time, there are less people to research global ocean simulation. Under the background of global environment simulation, the paper describes a solution for the application of global ocean simulation on the basis of previous work, and realizes the visualization system successfully using OSG (OpenSceneGraph) API and all kinds of open-source libraries, and demonstrates the result of cross-sea flight. At last, we analysis the deficiencies of our solution and make some improvement suggestions. ©2010 IEEE.

关键词： Computer hardware

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Brain MRI Segmentation with Patch-based CNN Approach

Brain MRI Segmentation with Patch-based CNN Approach

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第35届中国控制会议

作者： Zhipeng CUI Jie YANG Yu QIAO Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University The Key Laboratory of Ministry of Education for System Control and Information Processing

ISBN: (纸本)9781509009107

Brain Magnetic Resonance image(MRI) plays a non-substitutive role in clinical *** symptom of many diseases corresponds to the structural variants of *** structure segmentation in brain MRI is of great importance in modern medical *** methods were developed for automatic segmenting of brain MRI but failed to achieve desired *** this paper,we proposed a new patch-based approach for automatic segmentation of brain MRI using convolutional neural network(CNN).Each brain MRI acquired from a small portion of public dataset is firstly divided into *** of these patches are then used for training CNN,which is used for automatic segmentation of brain *** results showed that our approach achieved better segmentation accuracy compared with other deep learning methods.

关键词： CNN Deep Learning Brain MRI Segmentation Patch-based

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Multi-regularization parameters estimation for Gaussian mixture classifier based on MDL principle

Multi-regularization parameters estimation for Gaussian mixt...

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International Conference on Neural Computation Theory and Applications, NCTA 2011

作者： Zhou, Xiuling Guo, Ping Philip Chen, C.L. Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing 100875 China Artificial Intelligence Institute Beijing City University Beijing China Faculty of Science and Technology University of Macau Macau China

ISBN: (纸本)9789898425843

Regularization is a solution to solve the problem of unstable estimation of covariance matrix with a small sample set in Gaussian classifier. And multi-regularization parameters estimation is more difficult than single parameter estimation. In this paper, KLIM-L covariance matrix estimation is derived theoretically based on MDL (minimum description length) principle for the small sample problem with high dimension. KLIM-L is a generalization of KLIM (Kullback-Leibler information measure) which considers the local difference in each dimension. Under the framework of MDL principle, multi-regularization parameters are selected by the criterion of minimization the KL divergence and estimated simply and directly by point estimation which is approximated by two-order Taylor expansion. It costs less computation time to estimate the multi-regularization parameters in KLIM-L than in RDA (regularized discriminant analysis) and in LOOC (leave-one-out covariance matrix estimate) where cross validation technique is adopted. And higher classification accuracy is achieved by the proposed KLIM-L estimator in experiment.

关键词： Parameter estimation

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