Search

In in-line digital holography, the background of the recorded images is sometimes much higher than the signal of interest. It can originates, for example, from the diffraction of dusts or fringes coming from multiple reflexions in the optical components. It is often correlated, nonstationary and not constant over time. Detecting a weak signal superimposed over such a background is challenging. Detection of the pattern then requires a statistical modeling of the background. In this work, spatial correlations are locally estimated based on several background images. A fast algorithm that computes detection maps is derived. The proposed approach is evaluated on images obtained from experimental data recorded with a holographic microscope.

International audience; In several areas of imaging, it is necessary to detect the weak signal of a known pattern superimposed over a background. Because of its temporal fluctuations, the background may be difficult to suppress. Detection of the pattern then requires a statistical modeling of the background. Due to difficulties related to (i) the estimation of the spatial correlations of the background, and (ii) the application of an optimal detector that accounts forthese correlations, it is common practice to neglect them. In this work, spatial correlations at the scale of an image patch are locally estimated based on several background images. A fast algorithm for the computation of detection maps is derived. The proposed approach is evaluated on images obtained from a holographic microscope.

The search for new exoplanets by direct imaging is a very active research topic in astronomy. The detection is particularly challenging because of the very high contrast between the host star and the companions. They thus remain hidden by a nonstationary background displaying strong spatial correlations. We propose a new algorithm named PACO (for PAtch COvariances) for reduction of differential imaging datasets. Contrary to existing approaches, we model the background correlations using a local Gaussian distribution that locally captures the spatial correlations at the scale of a patch of a few tens of pixels. The decision in favor of the presence or the absence of an exoplanet in then performed by a binary hypothesis test. The method is completely parameter-free and produces both stationary and statistically grounded detection maps so that the false alarm rate, the probability of detection and the contrast can be directly assessed without post-processing and/or Monte-Carlo simulations. We describe in a forthcoming paper its detailed principle and implementation. In this paper, we recall the principle of the PACO algorithm and we give new illustrations of its benefits in terms of detection capabilities on datasets from the VLT/SPHERE-IRDIS instrument. We also apply our algorithm on multi-spectral datasets from the VLT/SPHERE-IFS spectrograph. The performance of PACO is compared to state-of-the-art algorithms such as TLOCI and KLIP-PCA.

Lensless color microscopy is a recent 3D quantitative imaging method allowing to retrieve physical parameters characterizing microscopic objects spread in a volume. The main advantages of this technique are related to its simplicity, compactness, low sensitivity of the setup to vibrations and the possibility to accurately characterize objects. The cost-effectiveness of the method can be further increased using low-end laser diodes as coherent sources and CMOS color sensor equipped with a Bayer filter array. However, the central wavelength delivered by this type of laser is generally known only with a limited precision and can evolve because of its dependence on temperature and power supply voltage. In addition, Bayer-type filters of conventional color sensors are not very selective, resulting in spectral mixing (crosstalk phenomenon) of signals from each color channel. Ignoring these phenomena leads to significant errors in holographic reconstructions. We have proposed a maximum likelihood estimation method to calibrate the setup (central wavelength of the laser sources and spectral mixing introduced by the Bayer filters) using spherical objects naturally present in the field of view or added (calibration objects). This calibration method provides accurate estimates of the wavelengths and of the crosstalk, with an uncertainty comparable to that of a high-resolution spectrometer. To perform the image reconstruction from color holograms following the self-calibration of the setup, we describe a regularized inversion method that includes a linear hologram formation model, sparsity constraints and an edge-preserving regularization. We show on holograms of calibrated objects that the self-calibration of the setup leads to an improvement of the reconstructions.

International audience; Lensless color microscopy (also called in-line digital color holography) is a recent quantitative 3D imaging method used in several areas including biomedical imaging and microfluidics. By targetting cost effective and compact designs, the wavelength of the low-end sources used is only imprecisely known, in particular because of their temperature and power supply voltage dependence. This imprecision is the source of biases during the reconstruction step. An additional source of error is the crosstalk phenomenon, i.e., the mixture in color sensors of signals originating from different color channels. We propose to use a parametric inverse problem approach to achieve the self-calibration of a digital color holographic setup. This process provides an estimation of the central wavelengths and of the crosstalk. We show that taking the crosstalk phenomenon into account in the reconstruction step improves its accuracy.; La microscopie sans lentille couleur (également appelée holographie numérique couleur en ligne) est une méthode d'imagerie 3D quantitative récente utilisée dans plusieurs domaines, dont l'imagerie biomédicale et la microfluidique. Lorsqu'on s'intéresse à des conceptions bas coût et compactes, les longueurs d'ondes des sources utilisées ne sont pas connues précisemment, notamment en raison de leur dépendance à la température et à la tension d'alimentation. Cette imprécision est la source de biais lors de l'étape de reconstruction. Une source d'erreur supplémentaire qui peut être présente dans ce type de montage, est le phénomène de "cross talk", c'est-à-dire le mélange des signaux des différentes longueurs d'ondes sur les canaux RGB de la matrice de Bayer du capteur. Nous proposons d'utiliser une approche de type problèmes inverses paramétrique pour réaliser un auto-étalonnage de ce type de configuration holographique couleur bas coût. Cette approche fournit une estimation des longueurs d'ondes centrales des sources et du "cross-talk". Nous montrons que la prise en compte du phénomène de "cross-talk" dans la phase de reconstruction améliore sa précision.

In-line digital holography is a simple yet powerful tool to image absorbing and/or phase objects. Nevertheless, the loss of the phase of the complex wavefront on the sensor can be critical in the reconstruction process. The simplicity of the setup must thus be counterbalanced by dedicated reconstruction algorithms, such as inverse approaches, in order to retrieve the object from its hologram. In the case of simple objects for which the diffraction pattern produced in the hologram plane can be modeled using few parameters, a model fitting algorithm is very effective. However, such an approach fails to reconstruct objects with more complex shapes, and an image reconstruction technique is then needed. The improved flexibility of these methods comes at the cost of a possible loss of reconstruction accuracy. In this work, we combine the two approaches (model fitting and regularized reconstruction) to benefit from their respective advantages. The sample to be reconstructed is modeled as the sum of simple parameterized objects and a complex-valued pixelated transmittance plane. These two components jointly scatter the incident illumination, and the resulting interferences contribute to the intensity on the sensor. The proposed hologram reconstruction algorithm is based on alternating a model fitting step and a regularized inversion step. We apply this algorithm in the context of fluid mechanics, where holograms of evaporating droplets are analyzed. In these holograms, the high contrast fringes produced by each droplet tend to mask the diffraction pattern produced by the surrounding vapor wake. With our method, the droplet and the vapor wake can be jointly reconstructed.

International audience; The search for exoplanet is a very active topic in astronomy. Exoplanet detection by direct imaging requires both dedicated instruments to mask out the host star and careful image processing methods. Data processing is challenging because the exoplanet signal is very faint and hidden in a much stronger non-stationary background displaying strong spatial correlations. In contrast to previous detection methods, we explicitly model the spatial correlations of the background and design a completely unsupervised method that accounts for the background non-stationarity. From a time series of observations , we learn a local model of the distribution of background patches. Significant sources are then detected with a generalized likelihood ratio test. The sub-pixel location and flux of each detected exoplanet are estimated jointly to a refining of the background model. Each detected source is removed from the data, following an orthogonal matching pursuit strategy. The stopping criterion is based on a control of false alarms. We compare the proposed algorithm to three state-of-the-art exoplanet detection methods on datasets obtained with SPHERE instrument operating at the Very Large Telescope (VLT) in Chile. We show a drastic improvement of the sensibility and much fewer false detections.