12,339 research outputs found

    Burst Denoising with Kernel Prediction Networks

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    We present a technique for jointly denoising bursts of images taken from a handheld camera. In particular, we propose a convolutional neural network architecture for predicting spatially varying kernels that can both align and denoise frames, a synthetic data generation approach based on a realistic noise formation model, and an optimization guided by an annealed loss function to avoid undesirable local minima. Our model matches or outperforms the state-of-the-art across a wide range of noise levels on both real and synthetic data.Comment: To appear in CVPR 2018 (spotlight). Project page: http://people.eecs.berkeley.edu/~bmild/kpn

    Mitigation of Side-Effect Modulation in Optical OFDM VLC Systems

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    Side-effect modulation (SEM) has the potential to be a significant source of interference in future visible light communication (VLC) systems. SEM is a variation in the intensity of the light emitted by a luminaire and is usually a side-effect caused by the power supply used to drive the luminaires. For LED luminaires powered by a switched mode power supply, the SEM can be at much higher frequencies than that emitted by conventional incandescent or fluorescent lighting. It has been shown that the SEM caused by commercially available LED luminaires is often periodic and of low power. In this paper, we investigate the impact of typical forms of SEM on the performance of optical OFDM VLC systems; both ACO-OFDM and DCO-OFDM are considered. Our results show that even low levels of SEM power can significantly degrade the bit-error-rate performance. To solve this problem, an SEM mitigation scheme is described. The mitigation scheme is decision-directed and is based on estimating and subtracting the fundamental component of the SEM from the received signal. We describe two forms of the algorithm; one uses blind estimation while the other uses pilot-assisted estimation based on a training sequence. Decision errors, resulting in decision noise, limit the performance of the blind estimator even when estimation is based on very long signals. However, the pilot system can achieve more accurate estimations, thus better performance. Results are first presented for typical SEM waveforms for the case where the fundamental frequency of the SEM is known. The algorithms are then extended to include a frequency estimation step and the mitigation algorithm is shown also to be effective in this case

    Lack of clustering in low-redshift 21-cm intensity maps cross-correlated with 2dF galaxy densities

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    We report results from 21-cm intensity maps acquired from the Parkes radio telescope and cross-correlated with galaxy maps from the 2dF galaxy survey. The data span the redshift range 0.057<z<0.0980.057<z<0.098 and cover approximately 1,300 square degrees over two long fields. Cross correlation is detected at a significance of 5.18σ5.18\sigma. The amplitude of the cross-power spectrum is low relative to the expected dark matter power spectrum, assuming a neutral hydrogen (HI) bias and mass density equal to measurements from the ALFALFA survey. The decrement is pronounced and statistically significant at small scales. At k∼1.5k\sim1.5 hMpc−1 h \mathrm{Mpc^{-1}}, the cross power spectrum is more than a factor of 6 lower than expected, with a significance of 14.8 σ14.8\,\sigma. This decrement indicates either a lack of clustering of neutral hydrogen (HI), a small correlation coefficient between optical galaxies and HI, or some combination of the two. Separating 2dF into red and blue galaxies, we find that red galaxies are much more weakly correlated with HI on k∼1.5k\sim1.5 hMpc−1h \mathrm{Mpc^{-1}} scales, suggesting that HI is more associated with blue star-forming galaxies and tends to avoid red galaxies.Comment: 12 pages, 3 figures; fixed typo in meta-data title and paper author
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