Guy Le Besnerais

An interferometry imaging beauty contest

We present a formal comparison of the performance of algorithms used for synthesis imaging with optical/infrared long-baseline interferometers. Five different algorithms are evaluated based on their performance with simulated test data. Each set of test data is formatted in the OI-FITS format. The data are calibrated power spectra and bispectra measured with an array intended to be typical of existing imaging interferometers. The strengths and limitations of each algorithm are discussed.

Reconstruction method for weak-phase optical interferometry.

Current optical interferometers are affected by unknown turbulent phases on each telescope. We account for this lack of phase information by introducing system aberration parameters, and we solve the image reconstruction problem by minimizing an original joint criterion in the aberrations and in the object. We validate this method by means of simulations. Tests on experimental data are under way.

Convex approximation to the likelihood criterion for aperture synthesis imaging

Aperture synthesis allows one to measure visibilities at very high resolutions by coupling telescopes of reasonable diameters. We consider the case where visibility amplitudes and phase are measured separately. It leads to an estimation problem where the noise model yields a nonconvex data-likelihood criterion. We show how to optimally approximate the noise model while keeping the criterion convex. This approximation has been validated both on simulations and on experimental data.

eVO: A realtime embedded stereo odometry for MAV applications

The navigation of a miniature aerial vehicle (MAV) in GPS-denied environments requires a robust embedded visual localization method. In this paper, we describe a simple but efficient stereo visual odometry algorithm, called eVO, running onboard our quadricopter MAV at video-rate. The proposed eVO algorithm relies on a keyframe scheme which allows to decrease the estimation drift and to reduce the computational cost. We study quantitatively the influence of the main parameters of the algorithm and tune them for optimal performance on various datasets. The eVO algorithm has been submitted to the KITTI odometry benchmark [1] where it ranks first at the date of submission, with an average translational drift of 1.93% and an average angular drift of less than 0.076 degres/m. Besides, we have made several experiments with our MAV with egolocalization given by eVO, for instance for autonomous 3D environment modeling.

Statistical performance modeling for superresolution: a discrete data-continuous reconstruction framework

We address performance modeling of superresolution (SR) techniques. Superresolution consists in combining several images of the same scene to produce an image with better resolution and contrast. We propose a discrete data-continuous reconstruction framework to conduct SR performance analysis and derive a theoretical expression of the reconstruction mean squared error (MSE) as a compact, computationally tractable function of signal-to-noise ratio (SNR), scene model, sensor transfer function, number of frames, interframe translation motion, and SR reconstruction filter. A formal expression for the MSE is obtained that allows a qualitative study of SR behavior. In particular we provide an original outlook on the balance between noise and aliasing reduction in linear SR. Explicit account for the SR reconstruction filter is an original feature of our model. It allows for the first time to study not only optimal filters but also suboptimal ones, which are often used in practice.

ROBUST PROCESSING OF IMAGES SEQUENCES PRODUCED BY AN ADAPTIVE OPTICS RETINAL CAMERA

Retinal images sequences provided by adaptive optics instruments have to be processed before clinical exploitation. We present a robust procedure that accounts for non-translational motions and variable image quality to deliver improved reconstructed images.

Passive depth estimation using chromatic aberration and a depth from defocus approach

In this paper, we propose a new method for passive depth estimation based on the combination of a camera with longitudinal chromatic aberration and an original depth from defocus (DFD) algorithm. Indeed a chromatic lens, combined with an RGB sensor, produces three images with spectrally variable in-focus planes, which eases the task of depth extraction with DFD. We first propose an original DFD algorithm dedicated to color images having spectrally varying defocus blurs. Then we describe the design of a prototype chromatic camera so as to evaluate experimentally the effectiveness of the proposed approach for depth estimation. We provide comparisons with results of an active ranging sensor and real indoor/outdoor scene reconstructions.

Tomographic PIV: particles versus blobs

We present an alternative approach to tomographic particle image velocimetry (tomo-PIV) that seeks to recover nearly single voxel particles rather than blobs of extended size. The baseline of our approach is a particle-based representation of image data. An appropriate discretization of this representation yields an original linear forward model with a weight matrix built with specific samples of the systems point spread function (PSF). Such an approach requires only a few voxels to explain the image appearance, therefore it favors much more sparsely reconstructed volumes than classic tomo-PIV. The proposed forward model is general and flexible and can be embedded in a classical multiplicative algebraic reconstruction technique (MART) or a simultaneous multiplicative algebraic reconstruction technique (SMART) inversion procedure. We show, using synthetic PIV images and by way of a large exploration of the generating conditions and a variety of performance metrics, that the model leads to better results than the classical tomo-PIV approach, in particular in the case of seeding densities greater than 0.06 particles per pixel and of PSFs characterized by a standard deviation larger than 0.8 pixels.

Background first- and second-order modeling for point target detection

This paper deals with point target detection in nonstationary backgrounds such as cloud scenes in aerial or satellite imaging. We propose an original spatial detection method based on first- and second-order modeling (i.e., mean and covariance) of local background statistics. We first show that state-of-the-art nonlocal denoising methods can be adapted with minimal effort to yield edge-preserving background mean estimates. These mean estimates lead to very efficient background suppression (BS) detection. However, we propose that BS be followed by a matched filter based on an estimate of the local spatial covariance matrix. The identification of these matrices derives from a robust classification of pixels in classes with homogeneous second-order statistics based on a Gaussian mixture model. The efficiency of the proposed approaches is demonstrated by evaluation on two cloudy sky background databases.

A Prediction-Correction Approach for Real-Time Optical Flow Computation Using Stereo

Estimating the optical flow robustly in real-time is still a challenging issue as revealed by current KITTI benchmarks. We propose an original two-step method for fast and performant optical flow estimation from stereo vision. The first step is the prediction of the flow due to the ego-motion, efficiently conducted by stereo-matching and visual odometry. The correction step estimates the motion of mobile objects. Algorithmic choices are justified by empirical studies on real datasets. Our method achieves framerate processing on images of realistic size, and provides results comparable or better than methods having computation times one or two orders of magnitude higher.