ShearLab 3D: Faithful Digital Shearlet Transforms based on Compactly Supported Shearlets

Wavelets and their associated transforms are highly efficient when approximating and analyzing one-dimensional signals. However, multivariate signals such as images or videos typically exhibit curvilinear singularities, which wavelets are provably deficient of sparsely approximating and also of analyzing in the sense of, for instance, detecting their direction. Shearlets are a directional representation system extending the wavelet framework, which overcomes those deficiencies. Similar to wavelets, shearlets allow a faithful implementation and fast associated transforms. In this paper, we will introduce a comprehensive carefully documented software package coined ShearLab 3D (www.ShearLab.org) and discuss its algorithmic details. This package provides MATLAB code for a novel faithful algorithmic realization of the 2D and 3D shearlet transform (and their inverses) associated with compactly supported universal shearlet systems incorporating the option of using CUDA. We will present extensive numerical experiments in 2D and 3D concerning denoising, inpainting, and feature extraction, comparing the performance of ShearLab 3D with similar transform-based algorithms such as curvelets, contourlets, or surfacelets. In the spirit of reproducible reseaerch, all scripts are accessible on www.ShearLab.org.Comment: There is another shearlet software package (http://www.mathematik.uni-kl.de/imagepro/members/haeuser/ffst/) by S. H\"auser and G. Steidl. We will include this in a revisio

Astrophysical Dynamics 1999/2000: Merging Research and Education

The workshop `Astrophysical Dynamics 1999/2000' followed a homonymous advanced research course, and both activities were organized by me. In this opening paper of the proceedings book, I describe them and document their strong impact on the academic life of the local institutions. The advanced research course was open to graduate students, senior researchers, and motivated under-graduate students with good background in physics and mathematics. The course covered several multi-disciplinary issues of modern research on astrophysical dynamics, and thus also of interest to physicists, mathematicians and engineers. The major topic was gas dynamics, viewed in context with stellar dynamics and plasma physics. The course was complemented by parallel seminars on hot topics given by experts in such fields, and open to a wide scientific audience. In particular, I gave a friendly introduction to wavelets, which are becoming an increasingly powerful tool not only for processing signals and images but also for analysing fractals and turbulence, and which promise to have important applications to dynamical modelling of disc galaxies. The workshop was open to a wide scientific audience. The workshop with published proceedings book was, as a matter of fact, the innovative form of exam that I proposed for the advanced research course. The contributions were refereed and their quality is high on average, exceptionally high in a few cases. The advanced research course and the workshop all together produced great enthusiasm in the students and welcomed the participation of a hundred different people, which means an order of magnitude more than an average graduate course at Chalmers University of Technology and G\"oteborg University.Comment: opening paper; the proceedings book is in http://www.oso.chalmers.se/~romeo/PROCEEDINGS_BOOK_

Blind Curvelet based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments

The localized nature of curvelet functions, together with their frequency and dip characteristics, makes the curvelet transform an excellent choice for processing seismic data. In this work, a denoising method is proposed based on a combination of the curvelet transform and a whitening filter along with procedure for noise variance estimation. The whitening filter is added to get the best performance of the curvelet transform under coherent and incoherent correlated noise cases, and furthermore, it simplifies the noise estimation method and makes it easy to use the standard threshold methodology without digging into the curvelet domain. The proposed method is tested on pseudo-synthetic data by adding noise to real noise-less data set of the Netherlands offshore F3 block and on the field data set from east Texas, USA, containing ground roll noise. Our experimental results show that the proposed algorithm can achieve the best results under all types of noises (incoherent or uncorrelated or random, and coherent noise)

Cost-effective HPC clustering for computer vision applications

We will present a cost-effective and flexible realization of high performance computing (HPC) clustering and its potential in solving computationally intensive problems in computer vision. The featured software foundation to support the parallel programming is the GNU parallel Knoppix package with message passing interface (MPI) based Octave, Python and C interface capabilities. The implementation is especially of interest in applications where the main objective is to reuse the existing hardware infrastructure and to maintain the overall budget cost. We will present the benchmark results and compare and contrast the performances of Octave and MATLAB