publications

2025

1. MNRAS

   

   MROP: Modulated Rank-One Projections for compressive radio interferometric imaging

   Olivier Leblanc^*, Chung San Chu^*, Laurent Jacques, and Yves Wiaux

   2025

   Abs arXiv Bib

   

   The emerging generation of radio-interferometric (RI) arrays are set to form images of the sky with a new regime of sensitivity and resolution. This implies a significant increase in visibility data volumes, which for single-frequency observations will scale as \( \mathcal O(Q^2B) \)for \(Q \)antennas and \(B \)short-time integration intervals (or batches), calling for efficient data dimensionality reduction techniques. This paper proposes a new approach to data compression during acquisition, coined \emphmodulated rank-one projection (MROP). MROP compresses the \(Q\times Q \)batchwise covariance matrix into a smaller number \(P \)of random rank-one projections and compresses across time by trading \(B \)for a smaller number \(M \)of random modulations of the ROP measurement vectors. Firstly, we introduce a dual perspective on the MROP acquisition, which can either be understood as random beamforming, or as a post-correlation compression. Secondly, we analyse the noise statistics of MROPs and demonstrate that the random projections induce a uniform noise level across measurements independently of the visibility-weighting scheme used. Thirdly, we propose a detailed analysis of the memory and computational cost requirements across the data acquisition and image reconstruction stages, with comparison to state-of-the-art dimensionality reduction approaches. Finally, the MROP model is validated for monochromatic intensity imaging both in simulation and from real data, with comparison to the classical and \emphbaseline-dependent averaging (BDA) models, and using the uSARA optimisation algorithm for image formation. Our results suggest that the data size necessary to preserve imaging quality using MROPs is reduced to the order of image size, well below the original and BDA data sizes.

   @article{leblanc25,
     author = {Leblanc, Olivier and Chu, Chung San and Jacques, Laurent and Wiaux, Yves},
     title = {MROP: Modulated Rank-One Projections for compressive radio interferometric imaging},
     year = {2025},
     publisher = {MNRAS},
   }

2024

1. PhD Thesis

   

   Compressive and neural-representation strategies for inverse problems From interferometric imaging to diffraction tomography

   Olivier Leblanc

   UCLouvain, Oct 2024

   Abs PDF

   Computational imaging has revolutionized our capabilities to sense the environment, enabling a wide range of applications in domains like medical, biological, or radio-astronomical imaging. This thesis broadens the scope of the computational imaging framework in two main directions. First, the principle of compressive imaging—i.e. capturing the image information with few linear projections data—is applied to two interferometric imaging applications, namely multicore fiber lensless imaging and radio-interferometry. In both cases, it is shown that compressive imaging is possible with random projections applied at the level of the interfering elements, resulting in a linear sensing model involving Fourier subsampling and rank-one projections. In addition to the analysis of their computational complexities, the sensing models are accompanied by uniform recovery guarantees highlighting their sample complexities—the number of interfering elements and number of measurements required for image recovery. The theoretical sample complexities are confirmed numerically, and also experimentally for multicore fiber imaging. Second, contributions are brought to the field of diffraction tomography, proposing a combination of an implicit neural representation—a continuous image representation by a neural network—and a nonlinear (multiple-scattering) sensing model. Significant efforts are made in a review of the different ways to model electromagnetic wave diffraction through inhomogeneous media, leveraging first-order optimization methods to solve the subsequent linear system of equations. The reconstruction of the 3-D image through the weights of an implicit neural representation instead of discrete voxels is proposed for this nonlinear sensing model, demonstrating the benefit of (i) the nonlinearity over linear approximations of the model, and (ii) the continuous representation for handling rotations of the object. The drawbacks of the approach are highlighted and improvements necessary for experimental use are discussed.

2. IEEE TCI

   

   Compressive radio-interferometric sensing with random beamforming as rank-one signal covariance projections

   Olivier Leblanc, Yves Wiaux, and Laurent Jacques

   Sep 2024

   Abs arXiv Bib Code

   

   Radio-interferometry (RI) observes the sky at unprecedented angular resolutions, enabling the study of several far-away galactic objects such as galaxies and black holes. In RI, an array of antennas probes cosmic signals coming from the observed region of the sky. The covariance matrix of the vector gathering all these antenna measurements offers, by leveraging the Van Cittert-Zernike theorem, an incomplete and noisy Fourier sensing of the image of interest. The number of noisy Fourier measurements—or *visibilities*—scales as \( \mathcal O(Q^2B) \)for \(Q \)antennas and \(B \)short-time integration (STI) intervals. We address the challenges posed by this vast volume of data, which is anticipated to increase significantly with the advent of large antenna arrays, by proposing a compressive sensing technique applied directly at the level of the antenna measurements. First, this paper shows that *beamforming*—a common technique of dephasing antenna signals—usually used to focus some region of the sky, is equivalent to sensing a rank-one projection (ROP) of the signal covariance matrix. We build upon our recent work \citeleblanc24 to propose a compressive sensing scheme relying on random beamforming, trading the \(Q^2\)-dependence of the data size for a smaller number \( N_\mathrm p \)ROPs. We provide image recovery guarantees for sparse image reconstruction. Secondly, the data size is made independent of \(B \)by applying \( N_\mathrm m \)Bernoulli modulations of the ROP vectors obtained for the STI. The resulting sample complexities, theoretically derived in a simpler case without modulations and numerically obtained in phase transition diagrams, are shown to scale as \( \mathcal O(K) \)where \(K \)is the image sparsity. This illustrates the potential of the approach.

   @article{leblanc24b,
     author = {Leblanc, Olivier and Wiaux, Yves and Jacques, Laurent},
     title = {Compressive radio-interferometric sensing with random beamforming as rank-one signal covariance projections},
     month = sep,
     year = {2024},
     publisher = {IEEE TCI}
   }

3. JOSAA

   

   ADMM-inspired image reconstruction for terahertz off-axis digital holography

   Murielle Kirkove, Yuchen Zhao, Olivier Leblanc, Laurent Jacques, and Marc Georges

   Journal of the Optical Society of America A, Sep 2024

   Abs Bib

   

   Image reconstruction in off-axis Terahertz digital holography is complicated due to the harsh recording conditions and the non-convexity form of the problem. In this paper, we propose an inverse problem-based reconstruction technique that jointly reconstructs the object field and the amplitude of the reference field. Regularization in the wavelet domain promotes a sparse object solution. A single objective function combining the data-fidelity and regularization terms is optimized with a dedicated algorithm based on an Alternating Direction Method of Multipliers framework. Each iteration alternates between two consecutive optimizations using projections operating on each solution and one soft thresholding operator applying to the object solution. The method is preceded by a windowing process to alleviate artifacts due to the mismatch between camera frame truncation and periodic boundary conditions assumed to implement convolution operators. Experiments demonstrate the effectiveness of the proposed method, in particular, improvements of reconstruction quality, compared to two other methods.

   @article{kirkove2024admm,
     title = {ADMM-inspired image reconstruction for terahertz off-axis digital holography},
     author = {Kirkove, Murielle and Zhao, Yuchen and Leblanc, Olivier and Jacques, Laurent and Georges, Marc},
     journal = {Journal of the Optical Society of America A},
     volume = {41},
     number = {3},
     pages = {A1--A14},
     year = {2024},
     publisher = {Optica Publishing Group},
     url = {https://opg.optica.org/josaa/viewmedia.cfm?uri=josaa-41-3-A1}
   }

4. JOSS

   PyProximal-scalable convex optimization in Python

   Matteo Ravasi, Marcus Valtonen Örnhag, Nick Luiken, Olivier Leblanc, and Eneko Uruñuela

   Journal of Open Source Software, Sep 2024

   Abs Bib PDF Code

   

   A broad class of problems in scientific disciplines ranging from image processing and astrophysics, to geophysics and medical imaging call for the optimization of convex, non-smooth objective functions. Whereas practitioners are usually familiar with gradient-based algorithms, commonly used to solve unconstrained, smooth optimization problems, proximal algorithms can be viewed as analogous tools for non-smooth and possibly constrained versions of such problems. These algorithms sit at a higher level of abstraction than gradient-based algorithms and require a basic operation to be performed at each iteration: the evaluation of the so-called proximal operator of the functional to be optimized. PyProximal is a Python-based library aimed at democratizing the application of convex optimization to scientific problems; it provides the required building blocks (i.e., proximal operators and algorithms) to define and solve complex, convex objective functions in a high-level, abstract fashion, shielding users away from any unneeded mathematical and implementation details.

   @article{ravasi2024pyproximal,
     title = {PyProximal-scalable convex optimization in Python},
     author = {Ravasi, Matteo and {\"O}rnhag, Marcus Valtonen and Luiken, Nick and Leblanc, Olivier and Uru{\~n}uela, Eneko},
     journal = {Journal of Open Source Software},
     volume = {9},
     number = {95},
     pages = {6326},
     year = {2024},
   }

2023

1. Computational fluorescence imaging with multi-core fiber bundles–Towards high-speed imaging through bare optical fibers

   Siddharth Sivankutty, Stéphanie Guerit, Olivier Leblanc, Matthias Hofer, Géraud Bouwmans, and 3 more authors

   In The European Conference on Lasers and Electro-Optics, Sep 2023

   Bib

   @inproceedings{sivankutty2023computational,
     title = {Computational fluorescence imaging with multi-core fiber bundles--Towards high-speed imaging through bare optical fibers},
     author = {Sivankutty, Siddharth and Guerit, St{\'e}phanie and Leblanc, Olivier and Hofer, Matthias and Bouwmans, G{\'e}raud and Andresen, Esben Ravn and Jacques, Laurent and Rigneault, Herve},
     booktitle = {The European Conference on Lasers and Electro-Optics},
     pages = {ch\_10\_1},
     year = {2023},
     organization = {Optica Publishing Group},
   }

2. 

   CoLSI: Continuous Lippmann-Schwinger Intensity Diffraction Tomography

   Olivier Leblanc, Laurent Jacques, and Ulugbek Kamilov

   Sep 2023

   Poster
3. Interferometric single-pixel imaging with a multicore fiber

   Olivier Leblanc, Matthias Hofer, Siddharth Sivankutty, Hervé Rigneault, and Laurent Jacques

   ISCS, Jun 2023

   Abs arXiv Bib

   Lensless illumination single-pixel imaging with a multicore fiber (MCF) is a computational imaging technique that enables potential endoscopic observations of biological samples at cellular scale. In this work, we show that this technique is tantamount to collecting multiple symmetric rank-one projections (SROP) of an interferometric matrix—a matrix encoding the spectral content of the sample image. In this model, each SROP is induced by the complex sketching vector shaping the incident light wavefront with a spatial light modulator (SLM), while the projected interferometric matrix collects up to \( \mathcal O(Q^2) \)image frequencies for a \(Q\)-core MCF. While this scheme subsumes previous sensing modalities, such as raster scanning (RS) imaging with beamformed illumination, we demonstrate that collecting the measurements of \(M \)random SLM configurations—and thus acquiring \(M \)SROPs—allows us to estimate an image of interest if \(M \)and \(Q \)scale log-linearly with the image sparsity level This demonstration is achieved both theoretically, with a specific restricted isometry analysis of the sensing scheme, and with extensive Monte Carlo experiments. On a practical side, we perform a single calibration of the sensing system robust to certain deviations to the theoretical model and independent of the sketching vectors used during the imaging phase. Experimental results made on an actual MCF system demonstrate the effectiveness of this imaging procedure on a benchmark image.

   @article{leblanc23iscs23,
     author = {Leblanc, Olivier and Hofer, Matthias and Sivankutty, Siddharth and Rigneault, Hervé and Jacques, Laurent},
     title = {Interferometric single-pixel imaging with a multicore fiber},
     dial = {https://dial.uclouvain.be/pr/boreal/object/boreal:276904},
     month = jun,
     year = {2023},
     journal = {ISCS},
   }

4. IEEE TCI

   

   Interferometric Lensless Imaging: Rank-one Projections of Image Frequencies with Speckle Illuminations

   Olivier Leblanc, Matthias Hofer, Siddharth Sivankutty, Hervé Rigneault, and Laurent Jacques

   Jun 2023

   Abs arXiv Bib Code

   

   Lensless illumination single-pixel imaging with a multicore fiber (MCF) is a computational imaging technique that enables potential endoscopic observations of biological samples at cellular scale. In this work, we show that this technique is tantamount to collecting multiple symmetric rank-one projections (SROP) of an interferometric matrix—a matrix encoding the spectral content of the sample image. In this model, each SROP is induced by the complex sketching vector shaping the incident light wavefront with a spatial light modulator (SLM), while the projected interferometric matrix collects up to \( \mathcal O(Q^2) \)image frequencies for a \(Q\)-core MCF. While this scheme subsumes previous sensing modalities, such as raster scanning (RS) imaging with beamformed illumination, we demonstrate that collecting the measurements of \(M \)random SLM configurations—and thus acquiring \(M \)SROPs—allows us to estimate an image of interest if \(M \)and \(Q \)scale log-linearly with the image sparsity level This demonstration is achieved both theoretically, with a specific restricted isometry analysis of the sensing scheme, and with extensive Monte Carlo experiments. On a practical side, we perform a single calibration of the sensing system robust to certain deviations to the theoretical model and independent of the sketching vectors used during the imaging phase. Experimental results made on an actual MCF system demonstrate the effectiveness of this imaging procedure on a benchmark image.

   @article{leblanc23,
     author = {Leblanc, Olivier and Hofer, Matthias and Sivankutty, Siddharth and Rigneault, Hervé and Jacques, Laurent},
     title = {Interferometric Lensless Imaging: Rank-one Projections of Image Frequencies with Speckle Illuminations},
     month = jun,
     year = {2023},
     publisher = {IEEE TCI},
   }

5. An Interferometric view of Speckle Imaging

   Olivier Leblanc, Matthias Hofer, Siddharth Sivankutty, Hervé Rigneault, and Laurent Jacques

   Biomedical and Astronomical Signal Processing Conference - BASP23., Feb 2023

   First Prize Poster

   First Prize in the Student Poster Competition

2022

1. An Interferometric view of Speckle Imaging

   Olivier Leblanc, Matthias Hofer, Siddharth Sivankutty, Hervé Rigneault, and Laurent Jacques

   Workshop on Low-Rank Models and Applications - LRMA22., Sep 2022

   Poster

2021

1. IEEE

   

   An Affordable Pedagogical Setup for Wave-particule Duality and Applications to Chaotic Stadium Cavity

   Olivier Leblanc, Loïc Tadrist, Nicolas Moreau, Boris Brun, Tristan Gilet, and 1 more author

   Sep 2021

   First prize Abs DOI Bib PDF

   IEEE R8 Student Paper Contest 2021

   Walking droplets represent an ideal playground to explore wave-particle duality at the macroscopic scale. The proper control and measurement of such system requires several experimental tools that are not often easily affordable at undergraduate level. This paper proposes a complete low-cost and open-source experimental setup for walking droplets with a performance characterization. The setup is tested by examining the behaviour of droplets in the stadium billiard, a two-dimensional concave cavity that yields chaotic trajectories. Drastic differences between classical and quantum particles behaviour were observed in such geometry. In particular, the long-term evolution of walking droplets in a stadium billiard presents clear scarring patterns, informing on the existence of preferred "probable positions" within the billiard.

   @article{paper_master,
     author = {Leblanc, Olivier and Tadrist, Loïc and Moreau, Nicolas and Brun, Boris and Gilet, Tristan and Hackens, Benoît},
     booktitle = {2021 15th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS)},
     title = {An Affordable Pedagogical Setup for Wave-particule Duality and Applications to Chaotic Stadium Cavity},
     year = {2021},
     pages = {398-403},
     doi = {10.1109/TELSIKS52058.2021.9606385},
   }

2020

1. Master Thesis

   

   Comparison between the walking droplet and the electron behaviour inside a chaotic stadium cavity

   Olivier Leblanc

   Jun 2020

   First prize Abs Bib PDF Code

   IEEE Best Master Thesis Award 2020

   The walking droplet experiment has received particular attention in the last fifteen years because it represents the first known example of a macroscopic pilot-wave system that exhibits behaviours thought to be exclusive to the microscopic quantum realm. Most efforts about walking droplets have focused on the experimental analysis in a fluid mechanics framework and on its mathematical modeling. On the other hand, the stadium billiard, one of the first 2D concave chaotic geometries introduced by Bunimovich, has been actively studied for these last decades because of the drastic differences observed between classical and quantum particles behaviour in such geometry. Consequently, the objective of this master thesis is to analyze the behaviour of walking droplets inside the Bunimovich stadium billiard and compare it with the behaviours of classical and quantum particles in similar conditions. To fulfil this objective, a complete low-cost experimental setup has been developed for the observations of walking droplets, comprising : a fabricated bath stuck on the membrane of a loudspeaker for the vertical shaking, a 3-axis accelerometer providing real-time measurements on PC, a droplet generator based on a piezoelectric buzzer and a fixed camera recording top view images of the droplet motion which are post-processed with Matlab. The imperfect horizontality of the developed setup is shown to lead to an effective Faraday instability threshold lower than the scientific consensus for the same forcing parameters \( \Gamma_F,e = 2.3[\text g] < \Gamma_F = 4.144[\text g] \). As it prevents us to correctly estimate the memory parameter Me, the conducted experiments are instead described in terms of shaking amplitude Γ and estimated tilt angles of the bath θ and β. It is observed that the walking droplet long-term evolution in a stadium billiard presents a clear scarring pattern, informing on the existence of preferred probable positions of the droplet inside the billiard. The scarring pattern, while very similar to a typical shape found in quantum simulations, is surprisingly much more robust against forcing variations than the scars observed for electrons.

   @article{master_thesis,
     author = {Leblanc, Olivier},
     title = {Comparison between the walking droplet and the electron behaviour inside a chaotic stadium cavity},
     month = jun,
     year = {2020},
   }