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4th International conference on signal processing and Machine Learning, CONF-SPML 2024

作者： Niu, xinyue Zhou, Yixuan Gong, Zhaoyuan Viterbi School of Engineering University of Southern California Los Angeles90089 United States Shenzhen College of International Education Shenzhen518043 China Faculty of Engineering Hong Kong Polytechnic University Hong Kong999077 Hong Kong

ISBN: (纸本)9781510674721

In recent years, facial editing technology using style-gan has developed rapidly. This takes advantage of StyleGAN's powerful generator, but it still presents some problems in practical applications that have been widely identified and proposed solutions. PTI(Pivotal Tuning Inversion) is a technique to optimize generators, which was released in 2021 and is a relatively new method with good effects. But in the actual test, there are still some problems. In this work, two significant flaws regarding PTI were found when it was applied to editing human faces. It is confirmed that this negative effect is widespread and non-negligible in some cases. Following the original paper of PTI, this paper specifically investigates how these defects occur from two aspects. A method of tuning hyperparameters is raised to improve the output inversion image. In the end, a conjecture is proposed that a discriminator could be trained to help the machine learn human preferences, an approach that has the potential to minimize the impact due to feature loss. © 2024 SPIE.

关键词： image enhancement

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A Generic Real Time Autoencoder-Based Lossy image Compression 5

A Generic Real Time Autoencoder-Based Lossy Image Compressio...

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5th International conference on Communications, signal processing, and their applications (ICCSPA)

作者： Tawfik, Abdelrahman Hosny, Shehab Hisham, Sara Farouk, Ali Amr Mustafa, Doha Moaty, Samaa Abdel Gamal, Ahmed Salah, Khaled Ain Shams Univ Dept Elect & Commun Fac Engn Cairo Egypt Siemens Digital Ind Software Fremont CA 94538 USA

ISBN: (纸本)9781665482370

multimedia compression is a fundamental and significant research topic in the industrial field in the past several decades attempting to improve compression techniques. It is always a trade-off between size and quality where the growth rate of image, audio and video data is far beyond the improvement of the compression ratios achieved so far. Here, we are aiming to explore the potential of neural networks to achieve data compression, making use of multilayer neural networks providing a more efficient solution. In this paper, we present a lossy compression architecture, which utilizes the advantages of convolutional autoencoder (CAE) to replace the conventional transforms. Experimental results demonstrate that our method outperforms traditional coding algorithms, by achieving better compression ratios over the related work.

关键词： Neural Networks Compression image Compression Auto-encoders Lossy

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Multi-Threading Method for Rapid Tool Wear Detection Based on Integrating image Classification and Object Detection

Multi-Threading Method for Rapid Tool Wear Detection Based o...

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2024 conference on Spectral Technology and applications, CSTA 2024

作者： You, Zhichao Du, Yuheng Wang, xi Li, Ziteng Liu, Huan Li, Duo Company Ltd. Shanghai200240 China Center for Precision Engineering Harbin Institute of Technology 92 West Dazhi St. Harbin150001 China Center of Ultra-Precision Optoelectronic Instrumentation Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781510683082

This research aims to develop a multi-threading method for rapid tool wear detection by integrating image classification and object detection techniques to address the challenge of tool wear detection. The research proposes a two-stage method that leverages a fast image classification model (VGG-16) and a high-accuracy object detection model (YOLOv5) to enable efficient multi-threading detection of tool wear regions across a large number of the flank wear images. The experiment results reveal that, when the wear images account for less than 70% of the total, this method can achieve detection speeds exceeding that of YOLOv5 while maintaining comparable detection accuracy. © 2024 SPIE.

关键词： Object detection image classification image processing Data modeling Sensors Artificial neural networks Convolutional neural networks Education and training Engineering signal detection

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LIGHTWEIGHT DEEP DEBLURRING MODEL WITH DISCRIMINATIVE MULTI-SCALE FEATURE FUSION 30

LIGHTWEIGHT DEEP DEBLURRING MODEL WITH DISCRIMINATIVE MULTI-...

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30th IEEE International conference on image processing (ICIP)

作者： Lv, Jiangtao Pan, Jinshan Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781728198354

Although existing learning-based deblurring methods achieve significant progress, these approaches tend to require lots of network parameters and huge computational costs, which limits their practical applications. Instead of pursuing larger deep models for boosting deblurring performance, we propose a lightweight deep convolutional neural network with lower computational costs and comparable restoration performance, which is based on a multi-scale framework with an encoder and decoder network architecture. Specifically, we present an effective depth-wise separable convolution block (DSCB) as the fundamental building block of our method to reduce the model complexity. In addition, to better utilize the features from different scales, we develop a simple yet effective discriminative multi-scale feature fusion (DMFF) module for achieving high-quality results. Experimental results on the benchmarks show that our method is about 10x smaller than the state-of-the-art deblurring methods, e.g. MPRNet [1], in terms of model parameters and FLOPs while achieving competitive performance. The training code and models are available at https://***/cslvjt/LightweightDeblur.

关键词： Single image deblurring lightweight network multi-scale feature fusion

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Neural Photo-Finishing

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ACM TRANSACTIONS ON GRAPHICS 2022年第6期41卷 p1-15页

作者： Tseng, Ethan Zhang, Yuxuan Jebe, Lars Zhang, xuaner xia, Zhihao Fan, Yifei Heide, Felix Chen, Jiawen Princeton Univ Princeton NJ 08544 USA Adobe San Jose CA USA

image processing pipelines are ubiquitous and we rely on them either directly, by filtering or adjusting an image post-capture, or indirectly, as image signal processing (ISP) pipelines on broadly deployed camera systems. Used by artists, photographers, system engineers, and for downstream vision tasks, traditional image processing pipelines feature complex algorithmic branches developed over decades. Recently, image-to-image networks have made great strides in image processing, style transfer, and semantic understanding. The differentiable nature of these networks allows them to fit a large corpus of data;however, they do not allow for intuitive, fine-grained controls that photographers find in modern photo-finishing tools. This work closes that gap and presents an approach to making complex photo-finishing pipelines differentiable, allowing legacy algorithms to be trained akin to neural networks using first-order optimization methods. By concatenating tailored network proxy models of individual processing steps (e.g. white-balance, tone-mapping, color tuning), we can model a non-differentiable reference image finishing pipeline more faithfully than existing proxy image-to-image network models. We validate the method for several diverse applications, including photo and video style transfer, slider regression for commercial camera ISPs, photography-driven neural demosaicking, and adversarial photo-editing.

关键词： image processing photo-finishing raw processing

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LUT-Based Area-Optimized Accurate Multiplier Design for signal processing applications 4th

LUT-Based Area-Optimized Accurate Multiplier Design for Sign...

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4th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Satyanarayana, B.V.V. Lakshmi, B. Kanaka Sri Kumar, G. Prasanna Srinivas, K. Department of ECE Vishnu Institute of Technology Bhimavaram534202 India

ISBN: (纸本)9783031488870

Multipliers play a role in various aspects of smart cities, which can be used in many applications like Traffic management, energy management and environmental management etc. The wide variety of applications of multipliers are in the field of signal processing and image processing. FPGA design of multiplier is one of the complex tasks in Digital electronics. Most of the designs uses DSP blocks, these multipliers are complex and occupies much area in FPGA. Accurate multiplier design with low area on FPGA is the challenging task. The proposed method is accurate multiplier design, which is designed only using lookup table (LUT). The proposed design has low power and reduced area because of using simple LUT’s for generating partial product. The proposed accurate multipliers reduce 10% less Hardware on vertex 7 FPGA compared to existing designs. © 2024, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Field programmable gate arrays (FPGA)

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AG-Mono: A Monocular Dataset for Unmanned Air Vehicles 30

AG-Mono: A Monocular Dataset for Unmanned Air Vehicles

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30th IEEE signal processing and Communications applications conference (SIU)

作者： Simsek, Bugra Giden, Ibrahim Halil Bilge, Hasan Sakir ASELSAN Girisimcilik Merkezi Ankara Turkey Gazi Univ Ankara Turkey

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450928

One of the most important information needed while performing unmanned aerial vehicles (UAV) operations is about the platform location and the environment. Such platforms mostly use GNSS signals outdoors. However, in indoor areas where GNSS signals cannot be received or in situations where signals are jammed, it is not possible to obtain location information using these signals. For that reason, alternative navigation systems have become so crucial. One of the most preferred systems among navigation technologies is the visual simultaneous localization and mapping (vSLAM) method performed using RGB cameras on the UAVs. In this study, an open monocular image dataset called AG-Mono was created and published online to test the performance of vSLAM algorithms. This dataset was created at three different exposure times using a handheld platform, and it includes video sequences at 640x480 image resolution. The experimental area where the images were created is a closed corridor with 16.5 x 4.5 meters and four sharp corners.

关键词： Unmanned Aerial Vehicles (UAVs) Visual SLAM (vSLAM) Monocular image Dataset

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Multi scene infrared image processing based on fusion algorithm 6

Multi scene infrared image processing based on fusion algori...

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6th conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

作者： Wang, Shuwei xi, Youyou Yang, Jinbao Tong, xiaojie Yang, Chen Beijing Institute of Environmental Characteristics Beijing China 93114troops Beijing China

ISBN: (数字)9781510679733

ISBN: (纸本)9781510679726

Infrared imaging technology is widely used in military and civilian fields, but in practical applications, accurate and effective detection and tracking of infrared small targets is a bottleneck problem that needs to be solved urgently. In response to the problem that traditional algorithms are difficult to handle complex scenes with low signal-to-noise ratio and deep learning algorithms rely heavily on data, the proposed algorithm combines traditional algorithms with deep learning algorithms and is applied to detect and track infrared moving targets in various complex scenes, with resolutions ranging from 640 * 512 to 320 * 256 video sequences. At the same time, traditional algorithms include both single frame and multi frame detection methods. In order to avoid the problem of poor real-time performance, we selected the TMS320C6678 hardware platform and implemented simulation applications using a DSP+FPGA architecture. Experimental results have shown that this algorithm has excellent performance in object detection and tracking. © 2024 SPIE.

关键词： Learning algorithms

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Point Cloud Based In-vehicle Occupancy Detection Method by Using 77GHz mmWave Radar 2

Point Cloud Based In-vehicle Occupancy Detection Method by U...

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2nd IEEE International conference on signal, Information and Data processing, ICSIDP 2024

作者： Cao, xinda Liu, Jiangang Wu, Yulin Shao, Fengzhi Wang, Yubo Cui, Guolong Quzhou China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China

ISBN: (纸本)9798331515669

In recent years, with the introduction and development of vehicle-to-everything (V2x) and child presence detection (CPD), there's an increasing demand for in-vehicle perception systems. Millimeter-wave (mmWave) radar has become one of the mainstream sensors in this field for high accuracy, small size, and low power consumption. In this paper, we develop a real-time in-vehicle occupancy detection method based on 77GHz mmWave radar. Firstly, a pre-processing framework based on dual-path detection is used to obtain more point clouds from weak targets. Then density-based spatial clustering of applications with noise (DBSCAN) is applied to classify the occupants' point clouds. Finally, we propose a novel probabilistic population-assisted occupancy detection algorithm based on the long and short-term feedback results, which can suppress missed detection and false alarms well. Our algorithm has been successfully deployed onto the radar board and achieves an average accuracy rate of 99.10% under various complex scene experiments. © 2024 IEEE.

关键词： image segmentation

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Subspace Modeling Enabled High-Sensitivity x-Ray Chemical Imaging 48

Subspace Modeling Enabled High-Sensitivity X-Ray Chemical Im...

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48th IEEE International conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Li, Jizhou Chen, Bin Zan, Guibin Qian, Guannan Pianetta, Piero Liu, Yijin Stanford University Slac National Accelerator Laboratory United States City University of Hong Kong School of Data Science Hong Kong Max-Planck-Institut für Informatik Germany

ISBN: (纸本)9781728163277

Resolving morphological chemical phase transformations at the nanoscale is of vital importance to many scientific and industrial applications across various disciplines. The TxM-xANES imaging technique, by combining full-field transmission x-ray microscopy (TxM) and x-ray absorption near edge structure (xANES), has been an emerging tool that operates by acquiring a series of microscopy images with multi-energy x-rays and fitting to obtain the chemical map. Its capability, however, is limited by the poor signal-to-noise ratios due to system errors and low exposure illuminations for fast acquisition. In this work, by exploiting the intrinsic properties and subspace modeling of the TxM-xANES imaging data, we introduce a simple and robust denoising approach to improve the image quality, which enables fast and high-sensitivity chemical characterization. Extensive experiments on both synthetic and real datasets demonstrate the superior performance of the proposed method. © 2023 IEEE.

关键词： chemical imaging image restoration x-ray

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