Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network

Methods based on convolutional neural network (CNN) have demonstrated tremendous improvements on single image super-resolution. However, the previous methods mainly restore images from one single area in the low resolution (LR) input, which limits the flexibility of models to infer various scales of details for high resolution (HR) output. Moreover, most of them train a specific model for each up-scale factor. In this paper, we propose a multi-scale super resolution (MSSR) network. Our network consists of multi-scale paths to make the HR inference, which can learn to synthesize features from different scales. This property helps reconstruct various kinds of regions in HR images. In addition, only one single model is needed for multiple up-scale factors, which is more efficient without loss of restoration quality. Experiments on four public datasets demonstrate that the proposed method achieved state-of-the-art performance with fast speed

Resonant Tidal Excitations of Inertial Modes in Coalescing Neutron Star Binaries

We study the effect of resonant tidal excitation of inertial modes in neutron stars during binary inspiral. For spin frequencies less than 100 Hz, the phase shift in the gravitational waveform associated with the resonance is small and does not affect the matched filtering scheme for gravitational wave detection. For higher spin frequencies, the phase shift can become significant. Most of the resonances take place at orbital frequencies comparable to the spin frequency, and thus significant phase shift may occur only in the high-frequency band (hundreds of Hertz) of gravitational wave. The exception is a single odd-paity $m=1$ mode, which can be resonantly excited for misaligned spin-orbit inclinations, and may occur in the low-frequency band (tens of Hertz) of gravitational wave and induce significant (>> 1 radian) phase shift.Comment: Minor changes. 6 pages. Phys. Rev. D. in press (volume 74, issue 2

Binding between endohedral Na atoms in Si clathrate I; a first principles study

We investigate the binding nature of the endohedral sodium atoms with the ensity functional theory methods, presuming that the clathrate I consists of a sheaf of one-dimensional connections of Na@Si$_{24}$ cages interleaved in three perpendicular directions. Each sodium atom loses 30% of the 3s$^1$ charge to the frame, forming an ionic bond with the cage atoms; the rest of the electron contributes to the covalent bond between the nearest Na atoms. The presumption is proved to be valid; the configuration of the two Na atoms in the nearest Si$_{24}$ cages is more stable by 0.189 eV than that in the Si$_{20}$ and Si$_{24}$ cages. The energy of the beads of the two distorted Na atoms is more stable by 0.104 eV than that of the two infinitely separated Na atoms. The covalent bond explains both the preferential occupancies in the Si$_{24}$ cages and the low anisotropic displacement parameters of the endohedral atoms in the Si$_{24}$ cages in the [100] directions of the clathrate I.Comment: First page: Affiliation added to PDF and PS versio

End-to-End Learning of Video Super-Resolution with Motion Compensation

Learning approaches have shown great success in the task of super-resolving an image given a low resolution input. Video super-resolution aims for exploiting additionally the information from multiple images. Typically, the images are related via optical flow and consecutive image warping. In this paper, we provide an end-to-end video super-resolution network that, in contrast to previous works, includes the estimation of optical flow in the overall network architecture. We analyze the usage of optical flow for video super-resolution and find that common off-the-shelf image warping does not allow video super-resolution to benefit much from optical flow. We rather propose an operation for motion compensation that performs warping from low to high resolution directly. We show that with this network configuration, video super-resolution can benefit from optical flow and we obtain state-of-the-art results on the popular test sets. We also show that the processing of whole images rather than independent patches is responsible for a large increase in accuracy.Comment: Accepted to GCPR201

Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks

Fluorescence microscopy images usually show severe anisotropy in axial versus lateral resolution. This hampers downstream processing, i.e. the automatic extraction of quantitative biological data. While deconvolution methods and other techniques to address this problem exist, they are either time consuming to apply or limited in their ability to remove anisotropy. We propose a method to recover isotropic resolution from readily acquired anisotropic data. We achieve this using a convolutional neural network that is trained end-to-end from the same anisotropic body of data we later apply the network to. The network effectively learns to restore the full isotropic resolution by restoring the image under a trained, sample specific image prior. We apply our method to $3$ synthetic and $3$ real datasets and show that our results improve on results from deconvolution and state-of-the-art super-resolution techniques. Finally, we demonstrate that a standard 3D segmentation pipeline performs on the output of our network with comparable accuracy as on the full isotropic data