Deep Reflection Prior

Reflections are very common phenomena in our daily photography, which distract people's attention from the scene behind the glass. The problem of removing reflection artifacts is important but challenging due to its ill-posed nature. Recent learning-based approaches have demonstrated a significant improvement in removing reflections. However, these methods are limited as they require a large number of synthetic reflection/clean image pairs for supervision, at the risk of overfitting in the synthetic image domain. In this paper, we propose a learning-based approach that captures the reflection statistical prior for single image reflection removal. Our algorithm is driven by optimizing the target with joint constraints enhanced between multiple input images during the training stage, but is able to eliminate reflections only from a single input for evaluation. Our framework allows to predict both background and reflection via a one-branch deep neural network, which is implemented by the controllable latent code that indicates either the background or reflection output. We demonstrate superior performance over the state-of-the-art methods on a large range of real-world images. We further provide insightful analysis behind the learned latent code, which may inspire more future work

Initial measurements of black hole spin in GX 339-4 from Suzaku spectroscopy

We report on a deep Suzaku observation of the stellar-mass black hole GX 339-4 in outburst. A clear, strong, relativistically shaped iron emission line from the inner accretion disk is observed. The broadband disk reflection spectrum revealed is one of the most sensitive yet obtained from an accreting black hole. We fit the Suzaku spectra with a physically motivated disk reflection model, blurred by a new relativistic line function in which the black hole spin parameter is a variable. This procedure yielded a black hole spin parameter of a p. Joint modeling of these Suzaku spectra and prior XMM-Newton spectra obtained in two different 0.89 +/- 0.04 outburst phases yields a spin parameter of a = 0.93 +/- 0.01. The degree of consistency between these results suggests that disk reflection models allow for spin measurements that are not strongly biased by scattering effects. We suggest that the best value of the black hole spin parameter is a = 0.93 +/- 0.01 (statistical) +/- 0.04 (systematic). Although preliminary, these results represent the first direct measurement of nonzero spin in a stellar-mass black hole using relativistic line modeling

Prevention of Reflection Cracking in Asphalt Overlays with Structofors, Petromate, and Cerex, HR-158, 1977

This report presents construction methods and results using three reinforcing fabrics to prevent reflection cracking in an asphalt overlay. The original highway in the rural area was Portland Cement Concrete 20 feet wide. It was widened by adding 2 feet of asphaltic concrete 10 inches deep on each side prior to resurfacing. Data are presented for the widening joint and transverse cracks in the rural area and for the random cracking in the urban area

Estimation of the correlation structure of crustal velocity heterogeneity from seismic reflection data

Numerous sources of evidence point to the fact that heterogeneity within the Earth's deep crystalline crust is complex and hence may be best described through stochastic rather than deterministic approaches. As seismic reflection imaging arguably offers the best means of sampling deep crustal rocks in situ, much interest has been expressed in using such data to characterize the stochastic nature of crustal heterogeneity. Previous work on this problem has shown that the spatial statistics of seismic reflection data are indeed related to those of the underlying heterogeneous seismic velocity distribution. As of yet, however, the nature of this relationship has remained elusive due to the fact that most of the work was either strictly empirical or based on incorrect methodological approaches. Here, we introduce a conceptual model, based on the assumption of weak scattering, that allows us to quantitatively link the second-order statistics of a 2-D seismic velocity distribution with those of the corresponding processed and depth-migrated seismic reflection image. We then perform a sensitivity study in order to investigate what information regarding the stochastic model parameters describing crustal velocity heterogeneity might potentially be recovered from the statistics of a seismic reflection image using this model. Finally, we present a Monte Carlo inversion strategy to estimate these parameters and we show examples of its application at two different source frequencies and using two different sets of prior information. Our results indicate that the inverse problem is inherently non-unique and that many different combinations of the vertical and lateral correlation lengths describing the velocity heterogeneity can yield seismic images with the same 2-D autocorrelation structure. The ratio of all of these possible combinations of vertical and lateral correlation lengths, however, remains roughly constant which indicates that, without additional prior information, the aspect ratio is the only parameter describing the stochastic seismic velocity structure that can be reliably recovere

Deep Learning Techniques for Backscattering Vector Estimation in ToF Data

In this work we propose a new approach that is not already mentioned in the literature to correct the multi-path interference phenomenon which occurs in time-of-flight cameras. We introduce a deep learning approach to learn the typical reflection structure of the light in a real environment and use it as strong prior to estimate the shape of the time-dependent scene impulse response, called backscattering vector