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Joined LTS5, EPFL as scientist (post-doc)!
Published:
publications
Interferometric radio transient reconstruction in compressed sensing framework
Published in Société Française d'Astronomie et d'Astrophysique (SF2A), 2015
Abstract
Imaging by aperture synthesis from interferometric data is a well-known, but is a strong ill-posed inverse problem. Strong and faint radio sources can be imaged unambiguously using time and frequency integration to gather more Fourier samples of the sky. However, these imagers assumes a steady sky and the complexity of the problem increases when transients radio sources are also present in the data. Hopefully, in the context of transient imaging, the spatial and temporal information are separable which enable extension of an imager fit for a steady sky. We introduce independent spatial and temporal wavelet dictionaries to sparsely represent the transient in both spatial domain and temporal domain. These dictionaries intervenes in a new reconstruction method developed in the Compressed Sensing (CS) framework and using a primal-dual splitting algorithm. According to the preliminary tests in different noise regimes, this new” Time-agile”(or 2D-1D) method seems to be efficient in detecting and reconstructing the transients temporal dependence.
Recommended citation: M. Jiang, J. N. Girard, J. -L. Starck, S. Corbel, and C. Tasse. Interferometric radio transient reconstruction in compressed sensing framework. In Société Française d'Astronomie et d'Astrophysique (SF2A). 2015. arXiv:1512.06548. https://arxiv.org/abs/1512.06548
Compressed sensing and radio interferometry
Published in 23rd European Signal Processing Conference (EUSIPCO), 2015
Abstract
Radio interferometric imaging constitutes a strong ill-posed inverse problem. In addition, the next generation radio telescopes, such as the Low Frequency Array (LOFAR) and the Square Kilometre Array (SKA), come with an additional direction-dependent effects which impacts the image restoration. In the compressed sensing framework, we used the analysis and synthesis formulation of the problem and we solved it using proximal algorithms. A simple version of our method has been implemented within the LOFAR imager and has been validated on simulated and real LOFAR data. It demonstrated its capability to super-resolve radio sources, to provide correct photometry of point sources in a large field of view and image extended emissions with enhanced quality as compared to classical deconvolution methods. One extension of our method is to use the temporal information of the data to build a 2D-1D sparse imager enabling the detection of transient sources.
Recommended citation: M. Jiang, J. N. Girard, J. -L. Starck, S. Corbel, and C. Tasse. Compressed sensing and radio interferometry. In 2015 23rd European Signal Processing Conference (EUSIPCO), volume, 1646–1650. Aug 2015. doi:10.1109/EUSIPCO.2015.7362663. https://doi.org/10.1109/EUSIPCO.2015.7362663
Joint Multichannel Deconvolution and Blind Source Separation
Published in SIAM J. Imaging Sci., 2017
Abstract
Blind source separation (BSS) is a challenging matrix factorization problem that plays a central role in multichannel imaging science. In a large number of applications, such as astrophysics, current unmixing methods are limited since real-world mixtures are generally affected by extra instrumental effects like blurring. Therefore, BSS has to be solved jointly with a deconvolution problem, which requires tackling a new inverse problem: deconvolution BSS (DBSS). In this article, we introduce an innovative DBSS approach, called DecGMCA (deconvolved generalized morphological component analysis), based on sparse signal modeling and an efficient alternative projected least-squares algorithm. Numerical results demonstrate that the DecGMCA algorithm performs very well on simulations. It further highlights the importance of jointly solving BSS and deconvolution instead of considering these two problems independently. Furthermore, the performance of the proposed DecGMCA algorithm is demonstrated on simulated radio-interferometric data.
Recommended citation: Ming Jiang*, Jérôme Bobin, and Jean-Luc Starck. Joint multichannel deconvolution and blind source separation. SIAM Journal on Imaging Sciences, 10(4):1997–2021, 2017. doi: 10.1137/16M1103713. https://epubs.siam.org/doi/abs/10.1137/16M1103713
Joint Multichannel Deconvolution and Blind Source Separation
Published in Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop, 2017
Abstract
In the real world, current Blind Source Separation (BSS) methods are limited since extra instrumental effects like blurring have not been taken into account. Therefore, a more rigorous BSS has to be solved jointly with a deconvolution problem, yielding a new inverse problem: deconvolution BSS (DBSS). We introduce an innovative DBSS approach, called DecGMCA, which is based on sparse signal modeling and an efficient alternative projected least square algorithm. Numerical results demonstrate the performance of DecGMCA and highlight the advantage of jointly solving BSS and deconvolution instead of considering these two problems independently.
Recommended citation: Ming Jiang, Jérôme Bobin, and Jean-Luc Starck. Joint multichannel deconvolution and blind source separation. In Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop. 2017. http://spars2017.lx.it.pt/index_files/papers/SPARS2017_Paper_87.pdf
Sparse spatio-temporal imaging of radio transients: poster on-line
Published in Proceedings of the International Astronomical Union, 2017
Abstract
The next-generation radio telescopes such as LOFAR and SKA will give access to high time-resolution and high instantaneous sensitivity that can be exploited to study slow and fast transients over the whole radio window. The search for radio transients in large datasets also represents a new signal-processing challenge requiring efficient and robust signal reconstruction algorithms. Using sparse representations and the general ‘compressed sensing’ framework, we developed a 2D–1D algorithm based on the primal-dual splitting method. We have performed our sparse 2D–1D reconstruction on three-dimensional data sets containing either simulated or real radio transients, at various levels of SNR and integration times. This report presents a summary of the current level of performance of our method.
Recommended citation: J. Girard, M. Jiang, J-L. Starck, and S. Corbel. Sparse spatio-temporal imaging of radio transients: poster on-line. Proceedings of the International Astronomical Union, 14(S339):303–306, 2017. doi:10.1017/S1743921318003022. https://doi.org/10.1017/S1743921318003022
Fourier dimensionality reduction for fast radio transients
Published in International BASP Frontiers workshop, 2019
Abstract
In the context of next-generation radio interferometers, we are facing a big challenge of how to economically process data. The classical dimensionality reduction technique, averaging visibilities on time, may dilute fast radio transients (FRT). We propose a robust fast approximate SVD-based dimensionality reduction method for FRT imaging. For each time slice of FRT imaging, our dimensionality reduction defines a linear embedding operator to reduce the space spanned by the left singular vectors of the measurement operator and this operator can be fast obtained via a weighted fft on adjoint measurement operator instead of expensive SVD. The preliminary results showcase that the proposed dimensionality reduction can simultaneously reduce the data significantly and recover FRT correctly, while the averaging technique causes the FRT dilution problem.
Recommended citation: Ming Jiang, Vijay Kartik, Jean-Philippe Thiran, and Yves Wiaux. Fourier dimensionality reduction for fast radio transients. In International BASP Frontiers workshop 2019, number CONF. 2019. https://infoscience.epfl.ch/record/263873
Deep Post-Processing for Sparse Image Deconvolution
Published in Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop, 2019
Abstract
Variational-based methods are the state-of-the-art in sparse image deconvolution. Yet, this class of methods might not scale to large dimensions of interest in current high resolution imaging applications. To overcome this limitation, we propose to solve the sparse deconvolution problem through a two-step approach consisting in first solving (approximately and fast) an optimization problem followed by a neural network for “Deep Post Processing”(DPP). We illustrate our method in radio astronomy, where algorithms scalability is paramount due to the extreme data dimensions. First results suggest that DPP is able to achieve similar quality to state-of-the-art methods in a fraction of the time.
Recommended citation: Matthieu Terris, Abdullah Abdulaziz, Arwa Dabbech, Ming Jiang, Audrey Repetti, Jean-Christophe Pesquet, and Yves Wiaux. Deep post-processing for sparse image deconvolution. In Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop. 2019. https://researchportal.hw.ac.uk/en/publications/deep-post-processing-for-sparse-image-deconvolution
A Faceted Prior for Scalable Wideband Computational Imaging
Published in Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop, 2019
Abstract
Hyperspectral images exhibit strong spectral correlations, which can be exploited via a low-rankness and joint-sparsity prior when reconstructed from incomplete and noisy measurements. A state-of-theart solution consists in using a regularization term based on both the l2, 1 and the nuclear norms, which however does not scale well with large numbers of spectral channels and huge image sizes. To alleviate this issue, we propose a parallelizable faceted low-rankness and jointsparsity prior to improve the scalability of the associated imaging algorithm while preserving its reconstruction performance and better promoting local spectral correlations. We illustrate our approach on synthetic data in the context of radio-astronomy.
Recommended citation: Pierre-Antoine Thouvenin, Abdullah Abdulaziz, Ming Jiang, Audrey Repetti, and Yves Wiaux. A faceted prior for scalable wideband computational imaging. In Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop. 2019. https://researchportal.hw.ac.uk/en/publications/a-faceted-prior-for-scalable-wideband-computational-imaging
A Faceted Prior for Scalable Wideband Imaging: Application to Radio Astronomy
Published in IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019
Abstract
Wideband radio-interferometric (RI) imaging consists in estimating images of the sky across a whole frequency band from incomplete Fourier data. Powerful prior information is needed to regularize the inverse imaging problem. At the extreme resolution and dynamic range of interest to modern telescopes, image cubes will far exceed Terabyte sizes, with data volumes orders of magnitude larger, making image estimation a very challenging task. The computational cost and memory requirements of corresponding iterative image recovery algorithms are extreme and call for high parallelism. A data-splitting strategy was recently introduced to parallelize computations over data blocks within an advanced primal-dual convex optimization algorithm. Building on the same algorithm, we propose an image faceting approach that consists in splitting the image cube into 3D overlapping facets with their own prior, reducing the computational bottleneck from full image to facet size. Simulation results suggest our prior provides similar if not superior reconstruction quality to the corresponding state-of-the-art non-faceted approach, with facet parallelization offering acceleration and therefore increased potential of scalability to large data and image sizes.
Recommended citation: Pierre-Antoine Thouvenin, Abdullah Abdulaziz, Ming Jiang, Audrey Repetti, and Yves Wiaux. A faceted prior for scalable wideband imaging: application to radio astronomy. In 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). 2019. https://researchportal.hw.ac.uk/en/publications/a-faceted-prior-for-scalable-wideband-imaging-application-to-radi
Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA): when precision meets scalability
Published in arXiv, 2020
Abstract
Upcoming radio interferometers are aiming to image the sky at new levels of resolution and sensitivity, with wide-band image cubes reaching close to the Petabyte scale for SKA. Modern proximal optimization algorithms have shown a potential to significantly outperform CLEAN thanks to their ability to inject complex image models to regularize the inverse problem for image formation from visibility data. They were also shown to be scalable to large data volumes thanks to a splitting functionality enabling the decomposition of data into blocks, for parallel processing of block-specific data-fidelity terms of the objective function. In this work, the splitting functionality is further exploited to decompose the image cube into spatio-spectral facets, and enable parallel processing of facet-specific regularization terms in the objective. The resulting Faceted HyperSARA algorithm is implemented in MATLAB (code available on GitHub). Simulation results on synthetic image cubes confirm that faceting can provide a major increase in scalability at no cost in imaging quality. A proof-of-concept reconstruction of a 15 GB image of Cyg A from 7.4 GB of VLA data, utilizing 496 CPU cores on a HPC system for 68 hours, confirms both scalability and a quantum jump in imaging quality from CLEAN. Assuming slow spectral slope of Cyg A, we also demonstrate that Faceted HyperSARA can be combined with a dimensionality reduction technique, enabling utilizing only 31 CPU cores for 142 hours to form the Cyg A image from the same data, while preserving reconstruction quality. Cyg A reconstructed cubes are available online.
Recommended citation: Pierre-Antoine Thouvenin*#, Abdullah Abdulaziz#, Ming Jiang#, Arwa Dabbech#, Audrey Repetti#, Adrian Jackson, Jean-Philippe Thiran, and Yves Wiaux. Parallel faceted imaging in radio interferometry via proximal splitting (faceted hypersara): when precision meets scalability. 2020. arXiv:2003.07358. https://arxiv.org/abs/2003.07358
talks
Talk on Dark matter sparse reconstruction
Published:
teaching
Computer tutor (Monitorat)
Teaching assistant, IMT Atlantique (ex-Télécom Bretagne), Autumn, 2011
Conduct a weekly seminar to provide assistance to engineering students on computer and programming.