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A 4Kx2K@60fps Multifunctional Video Display Processor for High Perceptual image Quality

IEEE TRANSACTIONS ON CIRCUITS AND systems I-REGULAR PAPERS 2020年第2期67卷 451-463页

作者： Wang, Hang Li, xiang Han, Daqiang Yu, Shiquan Yin, Shouyi Wei, Shaojun Zheng, Nanning Zhang, Xuchong Wang, Tiancheng Li, Wenchang Chen, Qiubo Ren, Pengju Wu, xiaogang Sun, Hongbin Jiang, Zhiqiang Xi An Jiao Tong Univ Sch Microelect Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Xi An Jiao Tong Univ Xian 710049 Peoples R China Bytedance Inc Beijing 100084 Peoples R China Hisilicon Inc Wireless Digital Chipset Dev Dept Shanghai 201206 Peoples R China Changhong Elect Co Ltd Mianyang 621000 Sichuan Peoples R China Tsinghua Univ Inst Microelect Beijing 100084 Peoples R China

Video display processing is an indispensable module in a variety of multimedia systems. With the increase of video format to 4K $\times$ 2K& x0040;60fps, the design of efficient display processing circuit faces severe challenges. This paper presents a video display processor which supports a variety of video formats and integrates multiple advanced algorithms for perpetual image quality improvement. By leveraging algorithm and architecture co-design, this paper efficiently implements these computational and memory intensive display processing algorithms in a single chip. In particular, we describe three representative design modules in detail, including motion compensated frame rate up-conversion, content-adaptive contrast enhancement, and backlight local dimming. Extensive experiments demonstrate that the proposed design modules outperform the previous works in terms of either image quality or hardware efficiency. The overall chip is fabricated in GF 55nm CMOS technology, and can work at the maximum operating frequency of 594 MHz. The maximum input and output video formats reach up to 4Kx2K@60fps.

关键词： Video display processing ultra-high definition frame rate up-conversion contrast enhancement backlight local dimming

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A Time-Domain processing Framework for Airborne and Vehicle-Borne Microwave Photonic SAR with Resolution 0.02 m

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Lou, Yishan xing, Mengdao Lin, Hao Ma, Penghui Sun, Guang-Cai Li, Ruoming Xidian University National Key Laboratory of Radar Signal Processing Xi’an710071 China Xidian University Academy of Advanced Interdisciplinary Research Xi’an China Xidian University Hangzhou Institute of Technology Hangzhou311200 China Chinese Academy of Sciences National Key Laboratory of Microwave Imaging Aerospace Information Research Institute Beijing100190 China University of the Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100190 China

With the advancement of Microwave Photonic (MWP) synthetic aperture radar (SAR) technology, resolution has increased to 0.02 m, and platforms have expanded from airborne to vehicle-borne. Incorporating ultra-wideband, long synthetic aperture, and varied observation ranges presents two primary challenges for MWP SAR imaging: 1) The enhancement of two-dimensional (2-D) resolution renders the imaging process more susceptible to 2-D space-variant motion errors (SVMEs). 2) The expansion of application platforms, particularly close-range observation by vehicle-borne platforms, invalidates traditional imaging algorithms based on the far-field assumption. To address the challenges, a novel time-domain processing framework is proposed for both airborne and vehicle-borne MWP SAR systems. Firstly, we analyzes wavenumber spectrum resampling during the back-projection (BP) process, establishing a mapping relationship between phase errors in image and time domain. This allows for the estimation of trajectory deviation, enabling a rough estimation of the 2-D SVME. Subsequently, a motion compensation (MoCo) method, based on an overlapping sub-image configuration combined with fast ground Cartesian BPA (GCBPA), is introduced to enable imaging. This method solves the problem that MoCo method cannot be integrated with fast GCBPA. In the third stage, the relationship between azimuth phase error (APE) and 2-D phase error is established. Leveraging this relationship, a 2-D wavenumber domain autofocus method is developed to concurrently compensate for APE and nonsystematic range cell migration (NsRCM). Experimental validations on both airborne (0.03m) and vehicle-borne (0.02m) MWP SAR platforms data confirm the effectiveness and versatility of the proposed time-domain processing framework. © 1980-2012 IEEE.

关键词： image enhancement

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FPGA-based fast algorithm of correcting saturated pixel in image 19

FPGA-based fast algorithm of correcting saturated pixel in i...

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19th image processing: algorithms and systems Conference, IPAS 2021

作者： Fu, Jun xiao, Chuanli Huang, Jingyi Peng, Wenxue Wang, xinpei Xu, Zhen Wang, Yanheng Liu, Min Mou, Xuanqin Institute of Intelligent Computing and Data Communications Xi'an Jiaotong University China

This paper proposes a novel method to correct saturated pixels in images. This method is based on the YCbCr color space and separately corrects the chrominance and the luminance of saturated pixels. In this algorithm, the saturated image is processed on the scan line, which is beneficial to the hardware implementation and the correction effect is good. Through the results of the joint simulation of MATLAB and Modelsim, it can be concluded that the hardware algorithm of this paper can use less resources to achieve fast correction. This paper uses Altera DE4 high-level development platform for hardware implementation. The calculation results show that highspeed image and video processing by FPGA is feasible and efficient, and it can be done frame by frame for highdefinition video. It has broad practical application prospects. © 2021, Society for Imaging Science and Technology.

关键词： Field programmable gate arrays (FPGA)

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Blind image Quality Index with High-Level Semantic Guidance and Low-Level Fine-Grained Representation

SSRN

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SSRN 2024年

作者： Hu, Bo Zheng, Jia Li, Leida Gu, Ke Wang, Shuaijian Li, Weisheng Gao, xinbo Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Institute for Brain and Intelligence Guangyang Bay Laboratory Chongqing400064 China School of Artificial Intelligence Xidian University Xi’an710071 China Faculty of Information Technology Beijing University of Technology Beijing100124 China

image Quality Assessment (IQA) has received unprecedented attention due to the extensive applications in benchmarking image processing algorithms and systems. Despite great progress in IQA, most previous frameworks either utilized only high-level semantic features or simply stacked multi-level features to account for the distortions, resulting in limited performance. The initial visual perception is formed after that the information is processed by the primary visual cortex and the advanced visual cortex. However, the information transmission of the cerebral cortex is not unidirectional, that is, the higher brain area can affect the primary brain area in turn, regulate its sensitivity and preference, so as to help the brain to understand the external world more finely. Inspired by this, we propose a novel blind image quality index with high-level Semantic Guidance and low-level fine-grained Representation (SGRNet). First, the versatile backbone, called pyramid vision Transformer, is used to simulate the multilevel information processing in the brain, generating multilevel feature representation. Second, we propose a novel high-level semantic guidance module to simulate the feedback mechanism between levels of the visual cortex. Third, a simple but effective fine-grained feature extraction module is proposed for high-level information compensation and lower-level content perception. After this, an attention mechanism-based enhancement module is proposed to further learn the above two features respectively. Finally, a bilinear pooling-based regression module is proposed to integrate the enhanced features of the two parts and map them to the quality score. Extensive experiments on six challenging public datasets show that the proposed SGRNet can deal well with both the simulated and authentic distortions and achieves state-of-the-art performance. © 2024, The Authors. All rights reserved.

关键词： Brain

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Graph-Order Optimization Algorithm Based on Equal-in-Space Distance Model for High-Resolution image Matting 7

Graph-Order Optimization Algorithm Based on Equal-in-Space D...

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7th IEEE International Conference on Cloud Computing and Intelligence systems, CCIS 2021

作者： Feng, Fujian Huang, Han Liang, Yihui South China University of Technology School of Software Engineering Guangzhou510006 China Ministry ofEducation Guangzhou510006 China School ofData Science and Information Engineering Guizhou Minzu University Guiyang550025 China Zhongshan Institute University of Electronic Science and Technology of China School of Computer Science Zhongshan528400 China

ISBN: (纸本)9781665441490

image matting is an essential image processing technology. optimized-based image matting methods can significantly improve the alpha matte quality of high-resolution images. However, the local information of the foreground may be similar to the background, which causes the inversion problem of the alpha matte in the single-point optimized. In this paper, we propose an image matting mathematical model of the equal-in-space distance. The model transforms the high-resolution image matting problem into several small-scale combinatorial optimization problems according to the similarity among pixel features. Inspired by spanning tree, we propose a graph-order optimization strategy, which generates the optimization sequence of small-scale optimization problems according to the edge weight among graph nodes. In addition, we designed a graph-order optimization algorithm based on optimized information transfer to solve each node in the graph. Experimental results show that the proposed model solves the alpha matte inversion problem of single-point optimization matting. Besides, the proposed algorithm outperforms the state-of-the-art optimization algorithms for the high-resolution image matting problem. © 2021 IEEE.

关键词： image enhancement

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A Novel Approach to Grasping Control of Soft Robotic Grippers based on Digital Twin

A Novel Approach to Grasping Control of Soft Robotic Gripper...

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International Conference on Automation and Computing (ICAC)

作者： Tianyi xiang Borui Li Quan Zhang March Leach Enggee Lim School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798350360882

ISBN: (纸本)9798350360899

This paper has proposed a Digital Twin (DT) framework for real-time motion and pose control of soft robotic grippers. The developed DT is based on an industrial robot workstation, integrated with our newly proposed approach for soft gripper control, primarily based on computer vision, for setting the driving pressure for desired gripper status in real-time. Knowing the gripper motion, the gripper parameters (e.g. curvatures and bending angles, etc.) are simulated by kinematics modelling in Unity 3D, which is based on four-piecewise constant curvature kinematics. The mapping in between the driving pressure and gripper parameters is achieved by implementing OpenCV based image processing algorithms and data fitting. Results show that our DT-based approach can achieve satisfactory performance in real-time control of soft gripper manipulation, which can satisfy a wide range of industrial applications.

关键词： Three-dimensional displays Computational modeling Fitting Kinematics Soft robotics Industrial robots Real-time systems Mathematical models Workstations Grippers

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Bilinear interpolation algorithm based on gradient-weighted optimization

Bilinear interpolation algorithm based on gradient-weighted ...

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image processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： LinTong Zhao Kai Che Jian Lv Yun Zhou College of Optoelectronic Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665464697

To address the problems of instantaneous field of view (IFoV) error and blurred details of infrared division of focal plane (DoFP) polarization images, this paper proposes a Bilinear interpolation algorithm based on gradient-weight optimization. The improvement of the algorithm mainly involves the following aspects: (1) the interpolation image pixels are extended for the characteristics of infrared DoFP polarization images, and the gradient correlation between the image elements is effectively used, to improve the interpolation accuracy; (2) the weighting factor is introduced to reconstruct the image pixels with weighting ratios, which improves the self-adaptive ability of the algorithm. The experimental results show that the algorithm in this paper effectively solves the problem of IFoV error of infrared polarization images, significantly enhances the contrast and sharpness of the degree of polarization images and meet the real-time hardware processing requirements. Experimental results showed that the PSNR of light intensity image increased by 4.4949dB, 3.5762dB and 1.5059dB compared with Nearest, Bilinear and Bicubic algorithms; the PSNR of the degree of polarization image by improved interpolation algorithm in this paper was increased by 2.3652dB, 2.2304dB and 2.0945dB, and the SSIM was increased by 0.0824, 0.1131 and 0.0881, compared with the Nearest, Bilinear and Bicubic algorithms; the operation speed of polarization image by improved interpolation algorithm in this paper was increased by 19.57fps and 21.68fps compared with Bicubic convolution and Gaussian interpolation algorithms.

关键词： Interpolation Polarization Machine learning algorithms Correlation image edge detection Machine learning Real-time systems

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基于浮选泡沫图像预测精矿品位的研究进展

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金属矿山 2024年第2期 25-38页

作者：卜显忠杨怡琳宛鹤西安建筑科技大学资源工程学院陕西西安710055

随着人工智能技术在矿业生产的广泛应用,利用计算机视觉技术提高精矿品位预测的准确性和效率已成为必然趋势。在综述了传统图像处理算法和深度学习算法在精矿品位预测中的应用与发展历程基础上,并探讨了未来的发展趋势和挑战。传统图像... 详细信息

随着人工智能技术在矿业生产的广泛应用,利用计算机视觉技术提高精矿品位预测的准确性和效率已成为必然趋势。在综述了传统图像处理算法和深度学习算法在精矿品位预测中的应用与发展历程基础上,并探讨了未来的发展趋势和挑战。传统图像处理技术通过提取泡沫图像的尺寸、颜色、纹理和流速等特征,结合分水岭分割、颜色矩、灰度共生矩阵和局部点特征匹配等算法进行特征提取。这些特征在计算资源有限的场景中具有一定的应用价值,但在应对精矿品位预测任务时精度较低。深度学习技术通过构建合适的模型架构并利用大量数据进行训练,能够提取高层语义特征,具有较高的预测精度,与图形处理单元(GPU)等高效运算设备配合使用,可实现高性能和高效率的统一。介绍了支持向量机(SVM)、极限学习机(ELM)等机器学习算法以及多层感知器(MLP)、全连接层和多尺度特征融合等深度学习算法在特征映射和品位预测中的应用,以及深度学习模型的发展历程。最后综述了工业界视觉检测系统的应用现状,并从数据驱动模型、多模态数据融合、算法实时性和数据集规模等方面分析了该领域所面临的挑战和未来发展趋势。

关键词：精矿品位预测浮选泡沫图像处理计算机视觉深度学习

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SAR图像目标轮廓增强预处理模块设计

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系统工程与电子技术 2024年第12期46卷 4010-4017页

作者：龚峻扬付卫红刘乃安西安电子科技大学通信工程学院陕西西安710071

针对合成孔径雷达(synthetic aperture radar,SAR)图像中的3个通道数据均相同、以至于在使用基于深度学习的目标检测网络对其进行目标检测时会造成通道信息利用效率低下的问题,基于对SAR图像中各种目标物轮廓特点的研究,提出一种基于平... 详细信息

针对合成孔径雷达(synthetic aperture radar,SAR)图像中的3个通道数据均相同、以至于在使用基于深度学习的目标检测网络对其进行目标检测时会造成通道信息利用效率低下的问题,基于对SAR图像中各种目标物轮廓特点的研究,提出一种基于平滑和锐化滤波的通道扩展预处理算法模块,并将其命名为ORLM(Original,Roberts,Laplace,Mean)模块。所提算法可被封装、集成并应用于目标检测算法的数据读取程序,可将每个通道数据均相同的SAR图像进行通道扩展,并且保证扩展后的通道数据充分包含目标的全部轮廓信息。通过将有否搭配本预处理算法的目标检测网络在不同舰船目标检测数据集上进行训练、测试及对比实验,实验结果表明,所提预处理算法可被应用在各种目标检测算法中,起到提升检测准确性的作用,且不会明显降低检测实时性。

关键词：图像处理合成孔径雷达深度学习目标检测

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An Edge Traffic Flow Detection Scheme Based on Deep Learning in an Intelligent Transportation System

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2021年第3期22卷 1840-1852页

作者： Chen, Chen Liu, Bin Wan, Shaohua Qiao, Peng Pei, Qingqi Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan 430073 Peoples R China

An intelligent transportation system (ITS) plays an important role in public transport management, security and other issues. Traffic flow detection is an important part of the ITS. Based on the real-time acquisition of urban road traffic flow information, an ITS provides intelligent guidance for relieving traffic jams and reducing environmental pollution. The traffic flow detection in an ITS usually adopts the cloud computing mode. The edge of the network will transmit all the captured video to the cloud computing center. However, the increasing traffic monitoring has brought great challenges to the storage, communication and processing of traditional transportation systems based on cloud computing. To address this issue, a traffic flow detection scheme based on deep learning on the edge node is proposed in this article. First, we propose a vehicle detection algorithm based on the YOLOv3 (You Only Look Once) model trained with a great volume of traffic data. We pruned the model to ensure its efficiency on the edge equipment. After that, the DeepSORT (Deep Simple Online and Realtime Tracking) algorithm is optimized by retraining the feature extractor for multiobject vehicle tracking. Then, we propose a real-time vehicle tracking counter for vehicles that combines the vehicle detection and vehicle tracking algorithms to realize the detection of traffic flow. Finally, the vehicle detection network and multiple-object tracking network are migrated and deployed on the edge device Jetson TX2 platform, and we verify the correctness and efficiency of our framework. The test results indicate that our model can efficiently detect the traffic flow with an average processing speed of 37.9 FPS (frames per second) and an average accuracy of 92.0% on the edge device.

关键词： image edge detection Cloud computing Real-time systems Machine learning Object detection Intelligent transportation systems Computational modeling Intelligent transportation system edge computing deep learning traffic flow detection

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