Visualization of Astronomical Nebulae via Distributed Multi-GPU Compressed Sensing Tomography

Abstract

(a) (b) (c) (d) Fig. 1. The planetary nebula M2-9 is a typical example of a bipolar nebula. Its quasi-symmetric twin lobes of ionized material emanate from a binary star system in its center. Assuming axial symmetry, our reconstruction algorithm uses a single input image (a) to produce a high-resolution 3D visualization that closely resembles the original image when rendered from the same viewpoint (b). From a novel vantage point, the emission along the principal axis of the nebula accumulates and creates a luminous halo (c). As the vantage point approaches the symmetry axis, the received intensity further increases and the perceived shape of the nebula changes toward two entangled rings (d). The resolution of the reconstructed volume is 512 3 voxels. Original image: Bruce Balick (University of Washington), Vincent Icke (Leiden University, The Netherlands), Garrelt Mellema (Stockholm University), and NASA.Abstract-The 3D visualization of astronomical nebulae is a challenging problem since only a single 2D projection is observable from our fixed vantage point on Earth. We attempt to generate plausible and realistic looking volumetric visualizations via a tomographic approach that exploits the spherical or axial symmetry prevalent in some relevant types of nebulae. Different types of symmetry can be implemented by using different randomized distributions of virtual cameras. Our approach is based on an iterative compressed sensing reconstruction algorithm that we extend with support for position-dependent volumetric regularization and linear equality constraints. We present a distributed multi-GPU implementation that is capable of reconstructing high-resolution datasets from arbitrary projections. Its robustness and scalability are demonstrated for astronomical imagery from the Hubble Space Telescope. The resulting volumetric data is visualized using direct volume rendering. Compared to previous approaches, our method preserves a much higher amount of detail and visual variety in the 3D visualization, especially for objects with only approximate symmetry.

On the Fly Porn Video Blocking Using Distributed Multi-Gpu and Data Mining Approach

“…Many authors have explored the way of generating Multi node or distributed environment along with multiple GPUs and have used it for various purposes [9][10][11] [12]. Yi, Zhiwei and Huawei (2013) successfully implemented Distributed Multi-Node GPU Accelerated Parallel Rendering Scheme for Visualization Cluster Environment [10].…”

“…Wenger, Ament, Guthe and Lorenz (2012) experienced huge performance gain by exploiting combined graphics memory in distributed cluster [12]. Wasif and Narayanan (2011) achieved 2 times speedup while using multi GPUs for K-means than on single GPU [13].…”

Visualizing Astronomical Data with Blender

“…Volume rendering, lighting, and shading play a key role in how data are presented (Gooch 1995;Ament et al 2010;Moloney et al 2011). Still, other experiments require new algorithms or innovative uses of existing hardware to push through challenging roadblocks that arise with new scientific paradigms (Wenger et al 2012;Kuchelmeister et al 2012). Hassan & Fluke (2011) review different approaches in astronomy looking to the future of the field as data rates increase.…”

Speech Compression and Reconstruction Algorithm Based on Multi-Scale Wavelet Packet and Compressed Sensing

“…At present, the application of the compressed sensing theory has got involved with many fields, such as: compressed sensing radar [2][3][4][5][6], Distributed Compressed Sensing (DCS) theory [7,8,9], wireless sensor network [10,11], image acquisition equipment development [12,13], medical image processing [14,15], Analog-to-Information [16,17], spectral analysis [18][19], hyperspec-tral image processing and remote sensing image processing [20,21,22]. However research on the compressed sensing of the speech signal is still in the early stage.…”