With the development of computer graphics and imageprocessing, the virtual scene generation and splicing technology has been widely used in various fields of computer-aided image analysis. In this context, this study...
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This study aims to speed up the method of image segmentation for humans, based on Mask2Former, to real-time. We propose the Multi-Fusion Model, which boosts the overall model speed by 50% while maintaining good result...
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For real-time monitoring of hardware parameters during full screen processing tasks. This article designs a real-timeimage transmission system based on ARM, which can achieve the display of image information on the s...
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In the context of the Chandrayaan 3 Lunar Mission, this research paper introduces a real-timeimage retrieval and denoising system powered by autoencoders, designed to tackle the challenge of noisy space imagery. Leve...
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This study adopts an empirical approach to evaluate the efficacy of imageprocessing methods in conservation efforts for animals. The initial phase involves the collection of data from various sources within the natur...
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Remote sensing image target detection is one of the key technologies in the field of intelligent interpretation of remote sensing images, and it has significant application value in various areas, including military d...
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The need for efficient and reliable imageprocessing techniques has never been greater in medical imaging. The proposed work analyzes and designs parallel VLSI (Very Large-Scale Integration) architectures that meet th...
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Tropical cyclones (TC) are very destructive meteorological events that mostly affect coastal regions. For early warning and catastrophe management, TC intensity estimation is essential. One important source of real-ti...
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With the progress of economy and science and technology in recent years, overhead lines in transmission lines have gradually been replaced by high-voltage cables. Because the cable tunnel is an underground closed equi...
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With the recent advancements in Convolutional Neural Network (CNN) architectures designs, many imageprocessing tasks benefited from the design of such deep and complex networks including image super-resolution (SR). ...
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ISBN:
(数字)9781665496209
ISBN:
(纸本)9781665496209
With the recent advancements in Convolutional Neural Network (CNN) architectures designs, many imageprocessing tasks benefited from the design of such deep and complex networks including image super-resolution (SR). While deep and complex models achieve improved SR reconstruction, they lack the practicality in implementation for real-time applications, such as in mobile phones or online conferencing. This is due to the large number of parameters and excessive required multiply-accumulate operations (MACs). In this paper, an accurate real-time SR model structure is proposed. The proposed structure reduces the required number of MACs by performing all operations on low dimensional feature maps and reduces the model parameters by utilizing depthwise separable convolutional (DSC) layers. An efficient version of the recently introduced self-calibrated convolution with pixel attention (SC-PA) is introduced to further improve feature representation. Experimental results show that the proposed model improves performance in objective metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index, over similar complexity real-time SR models.
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