Inferring a high dynamic range (HDR) image from a single low dynamic range (LDR) input is an ill-posed problem where we must compensate lost data caused by under-/over-exposure and color quantization. To tackle this, we propose the first deep-learning-based approach for fully automatic inference using convolutional neural networks. Because a naive way of directly inferring a 32-bit HDR image from an 8-bit LDR image is intractable due to the difficulty of training, we take an indirect approach; the key idea of our method is to synthesize LDR images taken with different exposures (i.e., bracketed images ) based on supervised learning, and then reconstruct an HDR image by merging them. By learning the relative changes of pixel values due to increased/decreased exposures using 3D deconvolutional networks, our method can reproduce not only natural tones without introducing visible noise but also the colors of saturated pixels. We demonstrate the effectiveness of our method by comparing our results not only with those of conventional methods but also with ground-truth HDR images.

We present 850 µm imaging polarimetry data of the ρ Oph-A core taken with the Submillimeter Common-User Bolometer Array-2 (SCUBA-2) and its polarimeter (POL-2), as part of our ongoing survey project, BISTRO (Bfields In STar forming RegiOns). The polarization vectors are used to identify the orientation of the magnetic field projected on the plane of the sky at a resolution of 0.01 pc. We identify 10 subregions with distinct polarization fractions and angles in the 0.2 pc ρ Oph A core; some of them can be part of a coherent magnetic field structure in the ρ Oph region. The results are consistent with previous observations of the brightest regions of ρ Oph-A, where the degrees of polarization are at a level of a few percents, but our data reveal for the first time the magnetic field structures in the fainter regions surrounding the core where the degree of polarization is much higher (> 5%). A comparison with previous near-infrared polarimetric data shows that there are several magnetic field components which are consistent at near-infrared and submillimeter wavelengths. Using the Davis-Chandrasekhar-Fermi method, we also derive magnetic field strengths in several sub-core regions, which range from approximately 0.2 to 5 mG. We also find a correlation between the magnetic field orientations projected on the sky with the core centroid velocity components.

We present the 850 μm polarization observations toward the IC5146 filamentary cloud taken using the Submillimetre Common-User Bolometer Array 2 (SCUBA-2) and its associated polarimeter (POL-2), mounted on the James Clerk Maxwell Telescope, as part of the B-fields In STar forming Regions Observations. This work is aimed at revealing the magnetic field morphology within a core-scale (1.0 pc) hub-filament structure (HFS) located at the end of a parsec-scale filament. To investigate whether the observed polarization traces the magnetic field in the HFS, we analyze the dependence between the observed polarization fraction and total intensity using a Bayesian approach with the polarization fraction described by the Rice likelihood function, which can correctly describe the probability density function of the observed polarization fraction for low signal-to-noise ratio data. We find a power-law dependence between the polarization fraction and total intensity with an index of 0.56 in A V ∼20-300 mag regions, suggesting that the dust grains in these dense regions can still be aligned with magnetic fields in the IC5146 regions. Our polarization maps reveal a curved magnetic field, possibly dragged by the contraction along the parsec-scale filament. We further obtain a magnetic field strength of 0.5±0.2 mG toward the central hub using the Davis-Chandrasekhar-Fermi method, corresponding to a mass-to-flux criticality of ∼1.3±0.4 and an Alfvénic Mach number of <0.6. These results suggest that gravity and magnetic field are currently of comparable importance in the HFS and that turbulence is less important.

We present the results of dust emission polarization measurements of Ophiuchus-B (Oph-B) carried out using the Submillimetre Common-User Bolometer Array 2 (SCUBA-2) camera with its associated polarimeter (POL-2) on the James Clerk Maxwell Telescope in Hawaii. This work is part of the B-fields in Star-forming Region Observations survey initiated to understand the role of magnetic fields in star formation for nearby star-forming molecular clouds. We present a first look at the geometry and strength of magnetic fields in Oph-B. The field geometry is traced over ∼0.2 pc, with clear detection of both of the sub-clumps of Oph-B. The field pattern appears significantly disordered in sub-clump Oph-B1. The field geometry in Oph-B2 is more ordered, with a tendency to be along the major axis of the clump, parallel to the filamentary structure within which it lies. The degree of polarization decreases systematically toward the dense core material in the two sub-clumps. The field lines in the lower density material along the periphery are smoothly joined to the large-scale magnetic fields probed by NIR polarization observations. We estimated a magnetic field strength of 630 ± 410 μG in the Oph-B2 sub-clump using a Davis–Chandrasekhar–Fermi analysis. With this magnetic field strength, we find a mass-to-flux ratio λ = 1.6 ± 1.1, which suggests that the Oph-B2 clump is slightly magnetically supercritical.

We present new observations of the active star formation region NGC 1333 in the Perseus molecular cloud complex from the James Clerk Maxwell Telescope B-Fields In Star-forming Region Observations (BISTRO) survey with the POL-2 instrument. The BISTRO data cover the entire NGC 1333 complex (∼1.5 pc×2 pc) at 0.02 pc resolution and spatially resolve the polarized emission from individual filamentary structures for the first time. The inferred magnetic field structure is complex as a whole, with each individual filament aligned at different position angles relative to the local field orientation. We combine the BISTRO data with low-and high-resolution data derived from Planck and interferometers to study the multiscale magnetic field structure in this region. The magnetic field morphology drastically changes below a scale of ∼1 pc and remains continuous from the scales of filaments (∼0.1 pc) to that of protostellar envelopes (∼0.005 pc or ∼1000 au). Finally, we construct simple models in which we assume that the magnetic field is always perpendicular to the long axis of the filaments. We demonstrate that the observed variation of the relative orientation between the filament axes and the magnetic field angles are well reproduced by this model, taking into account the projection effects of the magnetic field and filaments relative to the plane of the sky. These projection effects may explain the apparent complexity of the magnetic field structure observed at the resolution of BISTRO data toward the filament network.

Metaballs are implicit surfaces widely used to model curved objects, represented by the isosurface of a density field defined by a set of points. Recently, the results of particle-based simulations have been often visualized using a large number of metaballs, however, such visualizations have high rendering costs. In this paper we propose a fast technique for rendering metaballs on the GPU. Instead of using polygonization, the isosurface is directly evaluated in a per-pixel manner. For such evaluation, all metaballs contributing to the isosurface need to be extracted along each viewing ray, on the limited memory of GPUs. We handle this by keeping a list of metaballs contributing to the isosurface and efficiently update it. Our method neither requires expensive precomputation nor acceleration data structures often used in existing ray tracing techniques. With several optimizations, we can display a large number of moving metaballs quickly.

We present the POL-2 850 μm linear polarization map of the Barnard1 clump in the Perseus molecular cloud complex from the B-fields In STar-forming Region Observations survey at the James Clerk Maxwell Telescope. We find a trend of decreasing polarization fraction as a function of total intensity, which we link to depolarization effects toward higher-density regions of the cloud. We then use the polarization data at 850 μm to infer the planeof-sky orientation of the large-scale magnetic field in Barnard1. This magnetic field runs north-south across most of the cloud, with the exception of B1-c, where it turns more east-west. From the dispersion of polarization angles, we calculate a turbulence correlation length of 5.0±2 5 (1500 au) and a turbulent-to-total magnetic energy ratio of 0.5±0.3 inside the cloud. We combine this turbulent-to-total magnetic energy ratio with observations of NH 3 molecular lines from the Green Bank Ammonia Survey to estimate the strength of the plane-of-sky component of the magnetic field through the Davis-Chandrasekhar-Fermi method. With a plane-of-sky amplitude of 120±60 μG and a criticality criterion λ c =3.0±1.5, we find that Barnard1 is a supercritical molecular cloud with a magnetic field nearly dominated by its turbulent component.

Edit propagation is a technique that can propagate various image edits (e.g., colorization and recoloring) performed via user strokes to the entire image based on similarity of image features. In most previous work, users must manually determine the importance of each image feature (e.g., color, coordinates, and textures) in accordance with their needs and target images. We focus on representation learning that automatically learns feature representations only from user strokes in a single image instead of tuning existing features manually. To this end, this paper proposes an edit propagation method using a deep neural network (DNN). Our DNN, which consists of several layers such as convolutional layers and a feature combiner, extracts strokeadapted visual features and spatial features, and then adjusts the importance of them. We also develop a learning algorithm for our DNN that does not suffer from the vanishing gradient problem, and hence avoids falling into undesirable locally optimal solutions. We demonstrate that edit propagation with deep features, without manual feature tuning, can achieve better results than previous work.