In radio interferometry, information about a small region of the sky is obtained in the form of samples in the Fourier transform domain of the desired image. Since this sampling is usually incomplete, the missing information has to be reconstructed using additional assumptions about the image. The emerging field of compressed sensing (CS) provides a promising new approach to this type of problem which is based on the supposed sparsity of natural images in some transform domain. We present a versatile CS-based image reconstruction framework called SparseRI, an interesting alternative to the CLEAN algorithm, that permits a wide choice of different regularizers for interferometric image reconstruction. The performance of our method is evaluated on simulated data as well as on actual radio interferometry measurements from the VLA, showing that our algorithm is able to reproduce the main features of the test sources. The proposed method is a first step towards an alternative reconstruction approach that may be able to avoid typical artifacts like negative flux regions, to work with large fields of view and non-coplanar baselines, to avoid the gridding process, and, in particular, to produce results not far from those achievable by human-assisted processing in CLEAN through an entirely automatic algorithm, making it especially well-suited for automated processing pipelines.

The theory of compressed sensing has a natural application in interferometric aperture synthesis. As in many real-world applications, however, the assumption of random sampling, which is elementary to many propositions of this theory, is not met. Instead, the induced sampling patterns exhibit a large degree of regularity. In this paper, we statistically quantify the effects of this kind of regularity for the problem of radio interferometry where astronomical images are sparsely sampled in the frequency domain. Based on the favorable results of our statistical evaluation, we present a practical method for interferometric image reconstruction that is evaluated on observational data from the Very Large Array (VLA) telescope.

Contemporary challenges in the production of digital planetarium shows include real-time rendering realism as well as the creation of authentic content. While interactive, live performance is a standard feature of professional digital-dome planetarium software today, support for physically correct rendering of astrophysical phenomena is still often limited. Similarly, the tools currently available for planetarium show production do not offer much assistance towards creating scientifically accurate models of astronomical objects. Our paper presents recent results from computer graphics research, offering solutions to contemporary challenges in digital planetarium rendering and modeling. Incorporating these algorithms into the next generation of dome display software and production tools will help advance digital planetariums toward make full use of their potential.

In radio interferometry, information about a small region of the sky is obtained in the form of samples in the Fourier transform domain of the desired image. Since this sampling is usually incomplete, the missing information has to be reconstructed using additional assumptions about the image. The emerging field of Compressed Sensing (CS) provides a promising new approach to this type of problem which is based on the supposed sparsity of natural images in some transform domain. We present a versatile CS-based image reconstruction framework called SparseRI, an interesting alternative to the CLEAN algorithm, that permits a wide choice of different regularisers for interferometric image reconstruction. The performance of our method is evaluated on simulated data as well as on actual radio interferometry measurements from the VLA, showing that our algorithm is able to reproduce the main features of the test sources. The proposed method is a first step towards an alternative reconstruction approach that may be able to avoid typical artefacts like negative flux regions, work with large fields of view and non-coplanar baselines, avoid the gridding process, and reduce the amount of manual work that is required in order to obtain best-quality results.

For the creation of a realistic 3 meter-sized relief globe of the Moon, a detailed height map of the entire lunar surface is required. Available height measurements of the Moon's surface are too coarse by a factor of 15 for this purpose. The only publicly available source of high-resolution information are photographic images from the Lunar Orbiter IV mission in 1967. We present a shape-from-shading approach to plausibly increase the resolution of existing low-resolution height data, based on a single high-resolution photographic mosaic image of the Moon. The presented reconstruction approach is designed to be robust with respect to frequent imperfections of the photographic imagery. Aside from the automatic reconstruction of a complete detailed lunar surface height map, we give a qualitative validation by the reconstruction of lunar surface details from close-up photographs of the Apollo 15 landing site.

In an interdisciplinary German-Mexican research project partially funded by German DFG (Deutsche Forschungsgemeinschaft, grants MA 2555/7-1 and 444 MEX-113/25/0-1) and Mexican CONACyT (Consejo Nacional de Ciencia y Tecnología, grants 49447 and UNAM DGAPA-PAPIIT IN108506-2), we are currently evaluating different approaches for automatical reconstruction of plausible three-dimensional models of planetary nebulae. The team comprises astrophysicists working on planetary nebula morphology as well as computer scientists experienced in the field of reconstruction and visualization of astrophysical objects.