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...
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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 qualit...
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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.
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 pr...
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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. In...
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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.
imageprocessing pipelines are ubiquitous and we rely on them either directly, by filtering or adjusting an image post-capture, or indirectly, as imagesignalprocessing (ISP) pipelines on broadly deployed camera syst...
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imageprocessing pipelines are ubiquitous and we rely on them either directly, by filtering or adjusting an image post-capture, or indirectly, as imagesignalprocessing (ISP) pipelines on broadly deployed camera systems. Used by artists, photographers, system engineers, and for downstream vision tasks, traditional imageprocessing pipelines feature complex algorithmic branches developed over decades. Recently, image-to-image networks have made great strides in imageprocessing, 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.
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 multi...
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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 ...
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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.
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 ...
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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) ra...
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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 f...
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