Compressed Optical Imaging

We address the resolution of inverse problems where visual data must be recovered from incomplete information optically acquired in the spatial domain. The optical acquisition models that are involved share a common mathematical structure consisting of a linear operator followed by optional pointwise nonlinearities. The linear operator generally includes lowpass filtering effects and, in some cases, downsampling. Both tend to make the problems ill-posed. Our general resolution strategy is to rely on variational principles, which allows for a tight control on the objective or perceptual quality of the reconstructed data. The three related problems that we investigate and propose to solve are 1. The reconstruction of images from sparse samples. Following a non-ideal acquisition framework, the measurements take the form of spatial-domain samples whose locations are specified a priori. The reconstruction algorithm that we propose is linked to PDE flows with tensor-valued diffusivities. We demonstrate through several experiments that our approach preserves finer visual features than standard interpolation techniques do, especially at very low sampling rates. 2. The reconstruction of images from binary measurements. The acquisition model that we consider relies on optical principles and fits in a compressed-sensing framework. We develop a reconstruction algorithm that allows us to recover grayscale images from the available binary data. It substantially improves upon the state of the art in terms of quality and computational performance. Our overall approach is physically relevant; moreover, it can handle large amounts of data efficiently. 3. The reconstruction of phase and amplitude profiles from single digital holographic acquisitions. Unlike conventional approaches that are based on demodulation, our iterative reconstruction method is able to accurately recover the original object from a single downsampled intensity hologram, as shown in simulated and real measurement settings. It also consistently outperforms the state of the art in terms of signal-to-noise ratio and with respect to the size of the field of view. The common goal of the proposed reconstruction methods is to yield an accurate estimate of the original data from all available measurements. In accordance with the forward model, they are typically capable of handling samples that are sparse in the spatial domain and/or distorted due to pointwise nonlinear effects, as demonstrated in our experiments

SoK: Acoustic Side Channels

We provide a state-of-the-art analysis of acoustic side channels, cover all the significant academic research in the area, discuss their security implications and countermeasures, and identify areas for future research. We also make an attempt to bridge side channels and inverse problems, two fields that appear to be completely isolated from each other but have deep connections.Comment: 16 page

Optimisation d'un instrument chirurgical ultrasonique par algorithmes génétiques

Ce projet de semestre a pour objectif d'optimiser la conception d'un transducteur piézoélectrique ultrasonique, utilisé dans le domaine biomédical comme outil chirurgical, à l'aide d'une classe spécifique d'algorithmes appelés algorithmes génétiques

Diffraction-Unlimited Imaging Based on Conventional Optical Devices

International audienceWe propose a computational paradigm where off-the-shelf optical devices can be used to image objects in a scene well beyond their native optical resolution. By design, our approach is generic, does not require active illumination, and is applicable to several types of optical devices. It only requires the placement of a spatial light modulator some distance from the optical system. In this paper, we first introduce the acquisition strategy together with the reconstruction framework. We then conduct practical experiments with a webcam that confirm that this approach can image objects with substantially enhanced spatial resolution compared to the performance of the native optical device. We finally discuss potential applications, current limitations, and future research directions

Differential imaging forensics : a feasibility study

We motivate and develop a new line of digital forensics. In the meanwhile, we propose a novel approach to photographer identification, a rarely explored authorship attribution problem. We report a proof-of-concept study, which shows the feasibility of our method. Our contributions include a new forensic method for photographer de-anonymization and revealing a novel privacy threat which had been ignored before. The success of our creation builds on top of a new optical side-channel which we have discovered, as well as on how to exploit it effectively. We also make the first attempt to bridge side channels and inverse problems, two fields that appear to be completely isolated from each other but have deep connections

Differential imaging forensics

We introduce some new forensics based on differential imaging, where a novel category of visual evidence created via subtle interactions of light with a scene, such as dim reflections, can be computationally extracted and amplified from an image of interest through a comparative analysis with an additional reference baseline image acquired under similar conditions. This paradigm of differential imaging forensics (DIF) enables forensic examiners for the first time to retrieve the said visual evidence that is readily available in an image or video footage but would otherwise remain faint or even invisible to a human observer. We demonstrate the relevance and effectiveness of our approach through practical experiments. We also show that DIF provides a novel method for detecting forged images and video clips, including deep fakes

Hessian-Based Regularization for 3-D Microscopy Image Restoration

ABSTRACT We investigate a non quadratic regularizer that is based on the Hessian operator for dealing with the restoration of 3-D images in a variational framework. We show that the regularizer under study is a valid extension of the total-variation (TV) functional, in the sense that it retains its favorable properties while following a similar underlying principle. We argue that the new functional is well suited for the restoration of 3-D biological images since it does not suffer from the well-known staircase effect of TV. Furthermore, we present an efficient 3-D algorithm for the minimization of the corresponding objective function. Finally, we validate the overall proposed regularization framework through image deblurring experiments on simulated and real biological data