videoprocessing is a specific type of signal processing that frequently uses video filters and video files or video streams as both the input and output signals. In real-time applications like Bio-medical application...
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Recently, quality assessment for user-generated content (UGC) videos has become a challenging task due to the absence of reference videos and the presence of complex distortions. Prior methods has highlighted the effe...
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The requirements of multiple Unmanned Aerial Vehicle (UAV)-based video streaming transmission rapidly increase in flying ad-hoc networks (FANET). Due to diverse network features of FANET, tradeoff design in harsh netw...
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The proceedings contain 46 papers. The topics discussed include: continuous and non-contact extraction of respiratory signal and rate from thermal video;phonocardiogram signal denoising using dictionary learning;prese...
ISBN:
(纸本)9798331532543
The proceedings contain 46 papers. The topics discussed include: continuous and non-contact extraction of respiratory signal and rate from thermal video;phonocardiogram signal denoising using dictionary learning;presenting a method for gluing error detection using imageprocessing;alleviating undesired distance effect in spatio-temporal based video anomaly detection;real-time audio analysis for detection of apnea intervals in health-care system;classification of electric motors faults using Fourier-based features and self-organizing maps;validation of CMA simulation in analyzing the impact of electromagnetic waves on drone electronic boards;a hybrid approach for sentiment analysis of Arabic tweets;and line segmentation in Persian texts in double columns using hierarchical clustering algorithms.
Modern intelligent transport systems heavily rely on advanced analysis of road traffic data. This study presents a novel method for detecting vehicle speed that makes use of image and videoprocessing methods. The pro...
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We present a smartphone RGB camera based system for automatic human 3D posture monitoring driven by our proposed Machine Learning (ML) backbone. Rather than mapping RGB image sequences directly to 3D posture, we learn...
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The latest video coding standard, the Universal video Coding Standard (VVC), uses new coding tools to greatly improve compression efficiency. However, the adaptive QP module in the coding framework ignores the charact...
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In order to solve the problem of speed matching between image data output and acquisition, it is convenient to provide simple and flexible transmission for high-speed digital cameras and image acquisition cards. This ...
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This paper introduces the two-channel digital imageprocessing technology. The system uses FPGA as its core processing unit. Infrared thermal imager and CCD camera were used for the shooting. The FPGA composed of prog...
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Diffusion models are rising as a powerful solution for high-fidelity image generation, which exceeds GANs in quality in many circumstances. However, their slow training and inference speed is a huge bottleneck, blocki...
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ISBN:
(纸本)9798350301298
Diffusion models are rising as a powerful solution for high-fidelity image generation, which exceeds GANs in quality in many circumstances. However, their slow training and inference speed is a huge bottleneck, blocking them from being used in real-time applications. A recent DiffusionGAN method significantly decreases the models' running time by reducing the number of sampling steps from thousands to several, but their speeds still largely lag behind the GAN counterparts. This paper aims to reduce the speed gap by proposing a novel wavelet-based diffusion scheme. We extract low-and-high frequency components from both image and feature levels via wavelet decomposition and adaptively handle these components for faster processing while maintaining good generation quality. Furthermore, we propose to use a reconstruction term, which effectively boosts the model training convergence. Experimental results on CelebA-HQ, CIFAR-10, LSUN-Church, and STL-10 datasets prove our solution is a stepping-stone to offering real-time and high-fidelity diffusion models. Our code and pre-trained checkpoints are available at https://***/VinAIResearch/***.
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