CocoNet: A Deep Neural Network for Mapping Pixel Coordinates to Color Values

Bricman, Paul Andrei; Ionescu, Radu Tudor

doi:10.1007/978-3-030-04179-3_6

“…Real-time adaptive lossy image compression outperforms JPEG, JPEG2000 and WebP [121]. CocoNet is a deep learning approach that learns and maps pixel coordinates to colors [27]. A trained CocoNet-network is able to memorize one single picture and can be used for advanced image processing.…”

Section: Media Compressionmentioning

confidence: 99%

State of the Art and Future Trends in Data Reduction for High-Performance Computing

2020

JSFI

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Research into data reduction techniques has gained popularity in recent years as storage capacity and performance become a growing concern. This survey paper provides an overview of leveraging points found in high-performance computing (HPC) systems and suitable mechanisms to reduce data volumes. We present the underlying theories and their application throughout the HPC stack and also discuss related hardware acceleration and reduction approaches. After introducing relevant use-cases, an overview of modern lossless and lossy compression algorithms and their respective usage at the application and file system layer is given. In anticipation of their increasing relevance for adaptive and in situ approaches, dimensionality reduction techniques are summarized with a focus on non-linear feature extraction. Adaptive approaches and in situ compression algorithms and frameworks follow. The key stages and new opportunities to deduplication are covered next. An unconventional but promising method is recomputation, which is proposed at last. We conclude the survey with an outlook on future developments.

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“…Deep learning techniques have been applied in a wide range of tasks with remarkable results [10], [11]. One such task is image denoising, where deep learning achieved state-of-the-art results [12], outperforming classical approaches such as median or bilateral filtering. By transforming the radio signal into a spectrogram, the task of interference mitigation becomes similar to a task of image denoising.…”

Section: Related Workmentioning

confidence: 99%

Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation

Ristea¹,

Anghel²,

Ionescu³

2020

Preprint

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The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. In order to achieve remarkable results, cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of light conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. Cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of lighting conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. We propose two FCNs that take spectrograms of the beat signals as input, and provide the corresponding clean range profiles as output. We propose two architectures for interference mitigation which outperform the classical zeroing technique. Moreover, considering the lack of databases for this task, we release as open source a large scale data set that closely replicates real world automotive scenarios for single-interference cases, allowing others to objectively compare their future work in this domain. The data set is available for download at: http://github.com/ristea/arim.

show abstract

“…Deep learning techniques have been applied in a wide range of tasks with remarkable results [11], [12]. One such task is image denoising, where deep learning achieved state-of-the-art results [13], outperforming classical approaches such as median or bilateral filtering. By transforming the radio signal into a spectrogram, the task of interference mitigation becomes similar to a task of image denoising.…”

Section: Related Workmentioning

confidence: 99%

Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation

Ristea¹,

Anghel²,

Ionescu³

2020

Preprint

Self Cite

1

0

View full text Add to dashboard Cite

The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. In order to achieve remarkable results, cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of light conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. Cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of lighting conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. We propose two FCNs that take spectrograms of the beat signals as input, and provide the corresponding clean range profiles as output. We propose two architectures for interference mitigation which outperform the classical zeroing technique. Moreover, considering the lack of databases for this task, we release as open source a large scale data set that closely replicates real world automotive scenarios for single-interference cases, allowing others to objectively compare their future work in this domain. The data set is available for download at: http://github.com/ristea/arim.

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CocoNet: A Deep Neural Network for Mapping Pixel Coordinates to Color Values

Cited by 9 publications

References 34 publications

State of the Art and Future Trends in Data Reduction for High-Performance Computing

State of the Art and Future Trends in Data Reduction for High-Performance Computing

Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation

Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation

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