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A 4K$\times$ 2K@60fps Multifunctional Video Display Processor for High Perceptual image Quality

IEEE Transactions on Circuits and systems I: Regular Papers 2019年第2期67卷 451-463页

作者： Hang Wang Xuchong Zhang Tiancheng Wang Wenchang Li Qiubo Chen Pengju Ren xiaogang Wu Hongbin Sun Zhiqiang Jiang xiang Li Daqiang Han Shiquan Yu Shouyi Yin Shaojun Wei Nanning Zheng Institute of Artificial Intelligence and Robotics School of Microelectronics Xi’an Jiaotong University Xi’an China Xi’an Jiaotong University Xi’an China Bytedance Inc. Beijing China Wireless Digital Chipset Development Department Hisilicon Inc. Shanghai China Changhong Electric Co. Ltd. Mianyang China Institute of Microelectronics Tsinghua University Beijing China

Video display processing is an indispensable module in a variety of multimedia systems. With the increase of video format to 4K×2K@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 upconversion, 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 4K×2K@60fps.

关键词： Hardware Streaming media Computer architecture image quality Three-dimensional displays Very large scale integration TV

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DNN加速器技术发展及航空计算系统应用展望

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航空计算技术 2022年第3期52卷 130-134页

作者：赵一煊刘飞阳高晗王建生航空工业西安航空计算技术研究所陕西西安710068

新一代航空计算系统将以高性能智能数据处理为核心,能够支撑智能化的图像/雷达目标识别、大数据分析、指控决策、故障自诊断等多种应用场景。深度神经网络硬件加速器是面向人工智能领域专用的硬件加速平台,能够为深度卷积神经网络、循... 详细信息

新一代航空计算系统将以高性能智能数据处理为核心,能够支撑智能化的图像/雷达目标识别、大数据分析、指控决策、故障自诊断等多种应用场景。深度神经网络硬件加速器是面向人工智能领域专用的硬件加速平台,能够为深度卷积神经网络、循环神经网络等人工智能算法提供一个灵活可配置的运行环境,与通用计算硬件相比将获得高性能、低时延、低功耗等优势,其核心技术主要包括智能计算单元设计、并行计算加速结构、存储结构优化等。重点分析国内外深度神经网络硬件加速器技术发展情况,总结关键技术解决方案,探讨其在航空计算系统的应用场景及未来需要重点突破的技术方向,为新一代智能航空计算系统研制提供理论支撑。

关键词：航空电子系统智能计算深度神经网络硬件加速器

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注意力机制和自编码器构造的零水印算法

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计算机系统应用 2022年第9期31卷 257-264页

作者：李西明蔡河鑫陈志浩马莎杜治国吕红英华南农业大学数学与信息学院广州510642 华南农业大学电子工程学院广州510642

零水印技术为保护图像版权的有效手段之一.然而,现有的许多零水印算法大多采用传统的数学理论进行人工提取特征,在结合神经网络进行图片特征提取的零水印方向上并没有广泛研究.目前神经网络在图像特征提取上已经取得了很好的成绩,充分... 详细信息

零水印技术为保护图像版权的有效手段之一.然而,现有的许多零水印算法大多采用传统的数学理论进行人工提取特征,在结合神经网络进行图片特征提取的零水印方向上并没有广泛研究.目前神经网络在图像特征提取上已经取得了很好的成绩,充分利用卷积自编码器和注意力机制,提出了一种用于构造零水印的深度注意自编码器模型(attention mechanism and autoencoder, AMAE).首先是利用带有注意力的卷积神经网络构建自编码器,然后对自编码器进行训练;其次,利用训练好的编码器输出的特征构造图像的整体特征;最后,将获得的特征图进行二值模式处理得到特征二值矩阵,再与水印图像异或运算得到零水印,并在知识产权信息数据库进行注册,零水印一旦注册,原图像便处于水印技术的保护下.在训练过程中,借鉴对抗训练的思想,对模型进行加噪训练,这提高了模型的鲁棒性.实验结果表明,本文的零水印算法在旋转、噪声和滤波等攻击下,提取水印图像与原水印图像的归一化系数(normalized correlation, NC)值均超过0.9,证明了提出算法的有效性和优越性.

关键词：自编码器零水印鲁棒性注意力机制对抗训练深度学习

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Research of image Sharpness Assessment Algorithm for Autofocus

Research of Image Sharpness Assessment Algorithm for Autofoc...

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International Conference on image, Vision and Computing (ICIVC)

作者： Lilin Her xiaojun Yang Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi’an China

ISBN: (纸本)9781728123264

With the wide application of imaging system in security monitoring, aerospace, medical image and other fields, how to capture the clear image of the target in real time and automatically is particularly important. image sharpness evaluation function is the key to evaluate the imaging quality of various imaging systems. The spatial gradient evaluation algorithm is based on the direct processing of image pixels, and the calculation is simple and intuitive. In this paper, we compare the performance of image sharpness evaluation functions in four spatial domains through two sets of atlases with different background richness. Experiments show that Benner algorithm has high scene adaptability and strong anti-jamming ability; Laplace algorithm has high sensitivity and can get results quickly in different size images; Tenengrad algorithm can reduce the occurrence of local extremum after selecting a certain threshold; Robert algorithm has poor unimodality and accuracy in two sets of atlas test.

关键词： Sharpness evaluation function Autofocus Gradient operator Brenner algorithm auto-focus atlas images Sharpness image imagery (Psychotherapy) Imaging systems algorithms direct processing topographic maps

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Structural Health Monitoring system based on a concept of Lamb wave focusing by the piezoelectric array

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MECHANICAL systems AND SIGNAL processing 2018年 108卷 21-32页

作者： Kudela, Pawel Radzienski, Maciej Ostachowicz, Wieslaw Yang, Zhibo Polish Acad Sci Inst Fluid Flow Machinery Fiszera 14 St PL-80231 Gdansk Poland Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Shaanxi Peoples R China

Piezoelectric transducer arrays are utilized in Structural Health Monitoring systems as a means for excitation and sensing of elastic waves. Anomalies of propagating waves have enabled to develop damage detection algorithms. Depending on actuation-sensing strategies these algorithms can be classified as pitch-catch and pulse-echo. Despite many signal processing methods such as delay-sum, time-reversal, probability-based diagnostic imaging, etc. the spatial damage information provided by the actuator-sensor paths to reconstruct the damage image is limited. A novel strategy based on Lamb wave focusing is proposed in order to increase damage imaging resolution. In the proposed method all actuators are used at the same time exciting specially designed waveforms so that inspect one specific point of the structure. Damage map is created by applying appropriate signal processing. It uses dispersion curve of selected Lamb wave mode for dispersion compensation. The dispersion curve is acquired by using laser scanning Doppler vibrometer. The damage indicator is calculated based on the energy of compensated signals registered by sensors. It is shown that apart from high energy level at excitation point, energy is concentrated exactly in the damaged region. An example of crack detection and visualization in an aluminum plate is shown confirming the accuracy of the proposed method. Also the proposed method is compared to well-established delay-and-sum algorithm. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Lamb waves Wave focusing SHM PZT Sensor array Damage localization

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Non-planar calibration phantoms for optical coherence tomography 11

Non-planar calibration phantoms for optical coherence tomogr...

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Conference on Advanced Fabrication Technologies for Micro/Nano Optics and Photonics xi

作者： Lu, Yang Gordon, Neil Robinson, David Coldrik, Benjamin Mezentsev, Vladimir Menduni, Francesco Fratini, Antonio Sugden, Kate Aston Univ Aston Inst Photon Technol Birmingham B4 7ET W Midlands England Arden Photon Ltd Royston House267 Cranmore Blvd Solihull B90 4QT W Midlands England Optimec Ltd Unit B3 Spring Lane North Malvern WR14 1GF Worcs England Aston Univ Biomed Engn Birmingham B4 7ET W Midlands England

ISBN: (数字)9781510615748

ISBN: (纸本)9781510615748

Optical coherence tomography (OCT) is increasingly used in areas such as ophthalmology and contact lens metrology. However, in such cases, image distortion can occur due to the non-planar nature of the measured sample. Post-processing algorithms can be implemented to correct this distortion. Here we present an OCT phantom designed to confirm the validity of post-processing algorithms used for measuring curved surfaces. A multi-purpose OCT phantom has been created within a fused silica plano-convex lens using the direct femtosecond laser writing technique. This phantom can be used to calibrate and quantitatively assess the performance (e.g. resolution, sensitivity and distortion) of OCT systems and associated post-processing algorithms for curved structures such as lenses. This novel OCT phantom has been characterized using an optical microscope and OCT systems.

关键词： optical coherence tomography OCT image distortion femtosecond laser direct writing OCT phantoms OCT phantom fabrication

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Localization of Thyroid Nodules in Ultrasonic images 13th

Localization of Thyroid Nodules in Ultrasonic Images

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13th International Conference on Wireless algorithms, systems, and Applications (WASA)

作者： Yu, Ruiguo Liu, Kai Wei, xi Zhu, Jialin Li, Xuewei Wang, Jianrong Ying, xiang Yu, Zhihui Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China Tianjin Med Univ Canc Inst & Hosp Tianjin Peoples R China Tianjin Univ Sch Comp Software Tianjin Peoples R China

ISBN: (纸本)9783319942681;9783319942674

Thyroid nodule is a common clinical condition. Ultrasound is usually used to make a preliminary diagnosis, because it is convenient and cheap. Therefore, the study of thyroid ultrasound images of thyroid nodules has it's significance and value. This paper investigates the problem of locating thyroid nodules in ultrasound images by manual signs. The solution to this problem is divided into three parts: first, image processing processes the image preliminary and find the approximate location of the signs;then, sign recognition recognize the signs accurately using CNN models;finally, boundary adjustment is used for the final adjustment of the border. Experimental results show that the algorithm proposed in this paper can accurately locate the nodules in thyroid ultrasound images.

关键词： Thyroid nodule Ultrasonic image processing Convolutional neural network

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Automatic Target Recognition of Millimeter-Wave Radar Based on Deep Learning

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Journal of Physics: Conference Series 2021年第1期2031卷

作者： Chen xi Wang xin Chen Han Yu Zou Song He xiao Tang Air Force Early Warning Academy Wu Han Hu Bei 430014 China

All-day, all-weather wide-area search discovery target capability makes radar become a key piece of equipment in many military and civilian fields, and plays an indispensable role in tasks such as identification and positioning. However, at the same time, the current radar equipment generally relies on complex signal processing systems, and designing artificial feature extraction algorithms based on prior knowledge is both difficult and time-consuming, and it is difficult to fine-grained identify the target. In recent years, deep learning has been widely used in the radar field with its strong adaptability and strong self-learning ability. The paper presents a radar Doppler image based on YOLO v3 algorithm. With Millimeter-Wave radar distance-doppler reflectance image as input and darknet-53 as the feature extraction network, the automatic detection of vehicle millimeter wave radar cars, bicycles, pedestrians and trucks was realized, and the average detection accuracy reached 84.3%, providing new ideas and technical support for the development of intelligent radar and radar target detection.

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Cow tail detection method for body condition score using Faster R-CNN

Cow tail detection method for body condition score using Fas...

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International Conference on Unmanned systems and Artificial Intelligence

作者： xiaoping Huang xinru Li Zelin Hu Institute of Intelligent Machine Chinese Academy of Sciences University of Science and Technology of China Hefei China Institute of Intelligent Machine Chinese Academy of Sciences Hefei China

ISBN: (数字)9781728158594

ISBN: (纸本)9781728158600

Body condition score (BCS) is an important parameter to measure cow energy reserve for feeding management. Currently, measuring BCS mainly relies on veterinary experts or skilled scorers by observing and touching animals, which is time consuming and costly, especially on large-scale farms. In this study, a method of image processing and deep learning is employed to estimate cow BCS. Firstly, the network cameras were used to capture the back view images of the cows, resulting in 5470 images that constituted the sample data set, in which the key body parts (tail, pins and rump) of the cow were labeled manually. Secondly, Faster Region-Convolutional Neural Networks (Faster R-CNN) method was used to position and classify the cow tail images which were correlated to the value of BCS. Compared with other deep learning algorithms like the Single Shot multibox Detector (SSD), Faster R-CNN had a slightly higher accuracy. Specifically, the detection accuracy for cow tails was 84%, and the BCS classification had an average accuracy 70%. The low BCS classification rate was mainly due to the insufficient image data in this study for cows with certain ranges of BCS.

关键词： body condition score tail detection Faster R-CNN deep learning image processing dairy cows BCS1L gene cows image processing Machine Learning dairy cattle Body Composition Tail Data sampling

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Research on Aerial image Stitching Technology

Research on Aerial Image Stitching Technology

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International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)

作者： Ruirui Ma Chunlin Zhang Qing Guo Fangyi Wan School of Aeronautics Northwestern Polytechnical University Xi’an China

ISBN: (数字)9781728101996

ISBN: (纸本)9781728102009

The rise of UAV has made UAV play a major role in various industries, especially in place of manpower to solve difficult problems such as complex terrain detection and on-site monitoring in disaster areas. The pictures taken by the UAV are spliced to realize the global display of the scene, which is convenient for analysis and research. The integrity and clarity of image stitching depends on the performance of the stitching algorithm. The key to image registration and stitching is the extraction and matching of feature points. The article compares and analyzes the traditional three feature point (key-point) extraction algorithms. The article summarizes their advantages and disadvantages and scope of application. In this paper, SIFT, SURF and ORB algorithms were used respectively to extract feature points from the two images. Then, the nearest neighbor matching method was used to select the optimal matching points to remove the pseudo-matching points and improve the matching accuracy. After image registration, the composite image is prone to splicing gaps and brightness differences due to error accumulation, color differences, and the like. Therefore, in order to make the final panoramic image better, it is more necessary to perform image fusion after image registration processing, correct the difference, and eliminate the stitching gap. In this paper, the improved ORB algorithm combined with the weighted average fusion algorithm is used to achieve smooth transition of the two images. The improved algorithm time is reduced and the efficiency is significantly improved. The experimental results also show that the weighted average algorithm has high effectiveness and practicability in image fusion.

关键词： Feature extraction Robustness image registration Dogs Monitoring Real-time systems Remote sensing

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