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Research on Face Super Resolution Reconstruction Based on DBGAN

Research on Face Super Resolution Reconstruction Based on DB...

引用

International conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Yanjie Xu Xin li School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9781728146898

ISBN: (纸本)9781728146904

Super-resolution reconstruction technology is an important research topic in many fields such as image processing and computer vision. this technology can be used widely for security monitoring, old image reconstruction, image compression transmission and other fields. In this paper, super-resolution image reconstruction is performed on a low-resolution image of four times magnification. We propose the dense convolutional networks used as a generator instead of residual networks, and set perceptual loss as the optimization goal. We use the VGG network feature map as the loss function instead of Mean Square Error, which combines the perceptual loss with the adversarial loss and is beneficial for compensating the shortcomings of previous methods that lack high frequency detail. Experimental results show that our method can produce clearer face images than the traditional methods. these reconstructed images have higher resolution and Peak signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) than the images generated by the deep residual networks.

关键词： image reconstruction Convolution Residual neural networks Interpolation Generators

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Low-complexity rate-distortion optimal macroblock mode selection for MPEG-like video coders

Low-complexity rate-distortion optimal macroblock mode selec...

引用

IEEE International conference on Multimedia and Expo (ICME)

作者： Hyungjoon Kim Y. Altunbasak Center for Signal and Image Processing Georgia Institute of Technology Atlanta GA USA

Constrained optimization techniques provide an effective solution to the rate-distortion optimized mode selection in hybrid motion compensation-DCT based video encoders. However, the minimization of the Lagrangian cost function often presents a computational burden in real-time applications. In this paper, we propose a new method for rate distortion optimized coding mode selection. Instead of using the Lagrangian method, we employ a computationally efficient, yet highly effective rate-distortion model to minimize the overall distortion of a macroblock. Extensive experimental results show that the proposed method provides PSNR improvements of 2.79, 1.64 and 1.54 dB over TM5-based MPEG-2 encoder, at the bit-rates of 2, 4 and 6 Mbps, respectively, with negligible increase in computational complexity.

关键词： Rate-distortion image coding Discrete cosine transforms Constraint optimization Lagrangian functions Optimization methods PSNR Computational complexity Laplace equations signal processing

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Implementation of signal processing Approach for the Intensity Transformation in Autonomous Cars

Implementation of Signal Processing Approach for the Intensi...

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Sustainable Expert Systems (ICSES), International conference on

作者： Mubeena Mirshad Emmanuel Jose Ansula Rose Binish M. C KMEA Engineering College APJ Abdul Kalam Technological University Kerala India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Road transportation is a cornerstone of daily life for many, yet it also brings increased risks of accidents stemming from human error. Autonomous vehicles, driven by advancements in technology and artificial intelligence, offer a promising solution by enhancing road safety and security. Leading companies such as Google, Uber, and Tesla are heavily invested in the development of these self-driving vehicles. A critical technology underpinning autonomous cars is image processing. Cameras positioned at the vehicle's front capture data, enabling it to navigate autonomously. Effective lane detection is pivotal for these systems, involving the conversion of video input into images using techniques such as RGB to HSV thresholding. these advancements represent a significant leap towards safer and more efficient transportation systems for the future.

关键词： Technological innovation Navigation Transportation Data processing Real-time systems Road safety Safety Sensors Automobiles Autonomous vehicles

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Reconfigurable Frame Work for Video image Enhancement

Reconfigurable Frame Work for Video Image Enhancement

引用

2012 4th International conference on signal processing Systems(ICSPS 2012)

作者： Ravi Kumar Chadalavada Srivatsa S.K Department of ECE Prakasam Engineering College Kandukur.A.P India Department of ECE Anna University

this paper presents a method to improve the processing speed of video enhancement using FPGA-based hardware. Video enhancement is one of the most important and difficult components in video research. the aim of video enhancement is to improve the visual appearance of the video, or to provide a "better" transform representation for future automated video processing, such as analysis, detection, segmentation, recognition, surveillance, traffic, criminal justice systems. the development of video processing techniques is closely coupled to the video technology. With the advent of digital technology, the video signal can be digitalized into pixels and stored in a memory, which allows easy and flexible fetch and operation on the pixels to achieve more advanced video processing the VHDL hardware description language has been used in order to make possible a top-down design methodology. Real tine image processing in an application environment needs a set of low cost implementations of various algorithms. this paper presents an adaptive median filter based on FPGA and working at video rate. A video enhancement processor concept is proposed that enables efficient hardware implementation of enhancement procedures and hardware software co-design to achieve high performance, low-cost solutions. the processor runs on an FPGA prototyping board.

关键词： FPGA, VHDL, median filter, video enhancement

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Video Smoke Detection For Surveillance Cameras Based On Deep Learning In Indoor Environment

Video Smoke Detection For Surveillance Cameras Based On Deep...

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International conference on Recent Advances in signal processing, Telecommunications & Computing (SigTelCom)

作者： Viet thang Nguyen Cong Hoang Quach Minh Trien Pham VNU University of Engineering and Technology Ha Noi Viet Nam VNU University of Engineering and Technology Ha Noi Vietnam

ISBN: (数字)9781728168661

ISBN: (纸本)9781728168678

An early fire detection in indoor environment is essential for people's safety. During the past few years, many approaches using image processing and computer vision techniques were proposed. However, it is still a challenging task for application of video smoke detection in indoor environment, because the limitations of data for training and lack of efficient algorithms. the purpose of this paper is to present a new smoke detection method by using surveillance cameras. the proposed method is composed of two stages. In the first stage, motion regions between consecutive frames are located by using optical flow. In the second stage, a deep convolutional neural network is used to detect smoke in motion regions. Besides, to overcome the problem of lacking data, simulated smoke images are used to enrich the dataset. the proposed method is tested on our data set and real video sequences. Experiments show that the new method is successfully applied to various indoor smoke videos and significant for improving the accuracy of fire smoke detection. Source code and the dataset have been made available online.

关键词： Feature extraction Optical imaging Optical signal processing Training Computational modeling Indoor environments Detectors

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Taekwondo action recognition model based on deep learning and key point matching technology

Taekwondo action recognition model based on deep learning an...

引用

signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Jin Senbo Guangzhou Tianxing Experimental School Guangzhou China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

In order to solve the problems in Taekwondo action recognition and key point matching, this paper proposes a hybrid matching model that combines YOLOv8 and an improved dynamic time warping (DDTW) algorithm. the model uses YOLOv8 to detect key points in each frame of the video. When the feature points are lost due to occlusion, the model tries to use the information of adjacent frames to compensate. At the same time, in order to reduce jitter, the key point coordinate sequence is smoothed by a low-pass filter. the key frames are matched by the DDTW algorithm, and the action score is calculated according to the best path found by the algorithm. the scoring basis includes the cumulative cosine similarity, action fluency and accuracy. the experimental results show that by combining the images of the three perspectives, the model has achieved significant improvement in recognition accuracy, especially in the key frame combination extracted at the quartile position, the highest recognition accuracy of the front view and the right view reached 92.1% and 94.3% respectively. the method shows strong robustness in noise and speed change processing, and is suitable for motion analysis, rehabilitation training and sports science.

关键词： Training Accuracy image recognition Heuristic algorithms signal processing algorithms Jitter Solids Robustness Real-time systems Sports

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A recognition method for abnormal substation noise based on non-intrusive decomposition technology

A recognition method for abnormal substation noise based on ...

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New Energy and Power Engineering (ICNEPE), International conference on

作者： Ping Liu Ying Zhang Jianbo Shi Qini Zhu Liping Li Youyuan Fan Jie Xu State Grid Hubei Electric Power Co. Ltd. Wuhan China Electric Power Research Institute of State Grid Hubei Electric Power Co. Ltd. Wuhan China College of Electrical Engineering & New Energy China Three Gorges University Yichang China

ISBN: (数字)9798331516086

ISBN: (纸本)9798331516093

this study proposes a recognition method for abnormal substation noise based on non-intrusive decomposition technology. the aim is to achieve efficient separation and accurate identification of substation noise through advanced signal processing and deep learning technologies. Firstly, this method uses non-contact sound sensors to collect noise data at the plant boundary, including the operating noise of transformers and reactors. Subsequently, after data preprocessing and multi-scale decomposition of wavelet transform, combined with short-time Fourier transform (STFT) and Mel-frequency cepstral coefficients (MFCC) to extract time-frequency features, and then conduct joint analysis of time series and spectrum features through convolutional neural network (CNN). Finally, the model judges the noise state and distinguishes normal, early fault and fault states. Compared with traditional methods, this study has significant advantages in reducing monitoring costs, adapting to complex environments and accurately locating abnormal noise, providing an effective solution for fault detection of substation equipment.

关键词： wavelet transforms Time-frequency analysis Substations Costs Accuracy Noise Transformers Feature extraction wavelet analysis Convolutional neural networks

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Novel Approaches of Abdominal Computed Tomography image Super Resolution based on Deep Residual Learning 22

Novel Approaches of Abdominal Computed Tomography Image Supe...

引用

Proceedings of the 2022 4th International conference on Video, signal and image processing

作者： Chen Xie Fengze Li Kexin Hu School of Public Health Imperial College London United Kingdom Department of Electrical Engineering and Electronics University of Liverpool United Kingdom Department of Statistics University of Oxford United Kingdom

ISBN: (纸本)9781450397810

Few studies present how to implement super resolution (SR) on abdominal Computed Tomography (CT) images. However, in the field of medical imaging, the development of an advanced SR system will potentially improve the clinical diagnosis and prognosis of various diseases, enabling the medical image generation machine to communicate its information better. the efficient sub-pixel convolutional network (ESPCN) is considered as the baseline network. Successfully implementing a residual learning strategy and proposing an approach for incorporating categorical conditions as the prior in the baseline, we proposed three novel deep residual learning based networks to restore more realistic texture for pancreatic tumour regions in abdominal CT images. the quality of super-resolved images is evaluated quantitatively by using Peak signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). Our proposed networks show better SR performance compared with other existing deep learning based methods on the abdominal CT dataset.

关键词： super-resolution computed tomography medical imaging processing deep learning

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image Recognition of Small UAVs Based on Faster RCNN 2020

Image Recognition of Small UAVs Based on Faster RCNN

引用

Proceedings of the 2020 4th International conference on Vision, image and signal processing

作者： Jingbin Zhao Shengjun Wei Hui Xie Hao Zhong School of Computer Science Beijing Institute of Technology Beijing Beijing China

ISBN: (纸本)9781450389532

image recognition of small UAVs is the basis of anti-UAV technology based on photoelectric detection. In this paper, an automatic image recognition method for small UAVs based on Faster RCNN is proposed. the deep residual network is adopted to extract image features of small UAVs. then, the extracted features are input into the region proposal network, which can generate a region box that contains UAVs. the final recognition result is obtained through classification and regression. After training and testing the recognition model based on the dataset which contains thousands of images of common UAV on the market, the result shows that the recall of UAV recognition is 98%, the average precision is close to 97%, and the misdetection rate is low. the results of samples show that UAVs can be accurately recognized with less time, so the model owns good recognition performance.

关键词： Anti-UAV Low Slow and Small Targets image Recognition of Small UAVs Photoelectric Detection

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Siamese Network for Single image Rain Removal 2020

Siamese Network for Single Image Rain Removal

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Proceedings of the 2020 4th International conference on Vision, image and signal processing

作者： Yuxiang Xu Guomin Sun Jinsong Leng University of Electronic Science and Technology of China Sichuan Radio and TV University China University of Electronic Science and Technology of China China

ISBN: (纸本)9781450389532

this work focuses on removing rain streaks from the single image using the Siamese network. the Siamese network with an unique structure, where the inputs are paired images, are utilized to obtain the discriminative features of the input. We explore a method using the Siamese neural network to learn the difference between rainy images and clean images. the convolutional architecture is used for feature learning. Once the network has been tuned, the key feature of the rain is learned, then the rain streaks can be removed. By training the network on synthetic data, our framework can be generalized in the real rainy image tests. Numerical experiments show that the proposed method performs well in removing rain for the synthetic rainy images in terms of visual and quantitative measures.

关键词： Convolutional Network Rain Removal Siamese Network

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