Suchergebnisse - "deep convolutional autoencoder network"

Inpainting non-anatomical objects in brain imaging using enhanced deep convolutional autoencoder network von Kumar, Puranam Revanth, Shilpa, B, Jha, Rajesh Kumar, Raju, B Deevena, Mohammed, Thayyaba Khatoon

“… In this paper, we proposed a deep convolutional autoencoder network with improved parameters as a robust method for inpainting non-anatomical objects in MRI and CT images …”
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Broadband Communication Signal Detection Using Deep Convolutional Autoencoder and Spectrum Center Network von Wei, Bin

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Speaker Clustering by Co-Optimizing Deep Representation Learning and Cluster Estimation von Li, Yanxiong, Wang, Wucheng, Liu, Mingle, Jiang, Zhongjie, He, Qianhua

“… In our method, the deep representation feature is learned by a deep convolutional autoencoder network (DCAN …”
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Attention mechanism-based deep denoiser for desert seismic random noise suppression von Lin, Hongbo, Liu, Chang, Wang, Shigang, Ye, Wenhai

“… In order to recover the complex seismic events from low-frequency random noise, we propose an attention mechanism guided deep convolutional autoencoder network (ADCAE …”
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Segmentation of digital rock images using deep convolutional autoencoder networks von Karimpouli, Sadegh, Tahmasebi, Pejman

“… Segmentation is a critical step in Digital Rock Physics (DRP) as the original images are available in a gray-scale format. Conventional methods often use …”
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DeepGhost: real-time computational ghost imaging via deep learning von Rizvi, Saad, Cao, Jie, Zhang, Kaiyu, Hao, Qun

“… ”, using deep convolutional autoencoder network to achieve real-time imaging at very low sampling rates (10–20 …”
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Removing non-resonant background from broadband CARS using a physics-informed neural network von Muddiman, Ryan, O' Dwyer, Kevin, Camp, Jr, Charles H, Hennelly, Bryan

“… Recently, we demonstrated a deep convolutional autoencoder network, trained on pairs of simulated BCARS-Raman datasets, which could retrieve the Raman signal with high quality under ideal conditions …”
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Improving Imaging Quality of Real-time Fourier Single-pixel Imaging via Deep Learning von Rizvi, Saad, Cao, Jie, Zhang, Kaiyu, Hao, Qun

“… More specifically, a deep convolutional autoencoder network with symmetric skip connection architecture for real time 96 …”
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Deringing and denoising in extremely under-sampled Fourier single pixel imaging von Rizvi, Saad, Cao, Jie, Zhang, Kaiyu, Hao, Qun

“… To improve the imaging quality of real-time FSI, a fast image reconstruction framework based on deep convolutional autoencoder network (DCAN) is proposed …”
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Fast hyperspectral single-pixel imaging via frequency-division multiplexed illumination von Jiang, Xiaoyuan, Li, Ziwei, Du, Gang, Jia, Junlian, Wang, Qinghua, Chi, Nan, Dai, Qionghai

“… of the spatial light modulation speed. Additionally, we propose a multi-channel deep convolutional autoencoder network to reconstruct hyperspectral data from highly-compressed …”
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OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection von Abbasi, Saad, Famouri, Mahmoud, Shafiee, Mohammad Javad, Wong, Alexander

“… Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters …”
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Single-Pixel Imaging Based on Deep Learning Enhanced Singular Value Decomposition von Deng, Youquan, She, Rongbin, Liu, Wenquan, Lu, Yuanfu, Li, Guangyuan

“… The theoretical framework and the experimental implementation are elaborated and compared with the conventional methods based on Hadamard patterns or deep convolutional autoencoder network …”
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A deep autoencoder based approach for the inverse design of an acoustic-absorber von Mahesh, K., Ranjith, S. Kumar, Mini, R. S.

“… This paper proposes an algorithm to perform the inverse design of a low-frequency acoustic absorber using a deep convolutional autoencoder network …”
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Atmospheric Turbulence Removal in Long-Range Imaging Using a Data-Driven-Based Approach von Fazlali, Hamidreza, Shirani, Shahram, BradforSd, Michael, Kirubarajan, Thia

“… Atmospheric turbulence is one of the causes of quality degradation in long-range imaging and its removal from degraded frame sequences is considered an …”
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A Depth-Wise Separable U-Net Architecture with Multiscale Filters to Detect Sinkholes von Alshawi, Rasha, Hoque, Md Tamjidul, Flanagin, Maik C.

“… Numerous variants of the basic deep segmentation model—U-Net—have emerged in recent years, achieving reliable performance across different benchmarks. In this …”
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Spectral-Spatial Deep Support Vector Data Description for Hyperspectral Anomaly Detection von Li, Kun, Ling, Qiang, Qin, Yao, Wang, Yingqian, Cai, Yaoming, Lin, Zaiping, An, Wei

“… Hyperspectral anomaly detection (HAD) aims to distinguish anomalies from background-by-background modeling. Deep learning has been applied to HAD and achieves …”
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Hyperspectral unmixing using deep convolutional autoencoder von Elkholy, Menna M., Mostafa, Marwa, Ebied, Hala M., Tolba, Mohamed F.

“… In this paper, we address the linear unmixing problem with an unsupervised Deep Convolutional Autoencoder network (DCAE …”
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Single-pixel imaging with untrained convolutional autoencoder network von Li, Zhicai, Huang, Jian, Shi, Dongfeng, Chen, Yafeng, Yuan, Kee, Hu, Shunxing, Wang, Yingjian

“… •We propose a physical model-driven untrained deep convolutional autoencoder network for SPI and validate its performance from simulations and experiments …”
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A health indicator construction method of rolling bearing based on vibration image features and deep autoencoder network von Duan, Yong, Cao, Xiangang, Zhao, Jiangbin, Zhang, Ruiyuan, Yang, Xin, Guo, Xingyu

“… Rolling bearing is a key component and weak link of rotating machinery, the construction of health indicator (HI) with good performance can provide support for …”
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Deep-learned orthogonal basis patterns for fast, noise-robust single-pixel imaging von Ritz, Ann Aguilar, Dailisan, Damian

“… We present a modified deep convolutional autoencoder network (DCAN) for SPI on 64x64 pixel images with up to …”
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