Photo-detection correlation degrees of a two-photon source.

It is shown that the canonical correlation method used with the intrinsic degrees of coherence (IDOCs) to describe coherence properties of partially polarized light is effective to identify relevant properties of the photo-detection correlations of a two-photon source described with its density operator. The results are compared with other global correlation measures.

Extended-depth of field random illumination microscopy, EDF-RIM, provides super-resolved projective imaging.

The ultimate aim of fluorescence microscopy is to achieve high-resolution imaging of increasingly larger biological samples. Extended depth of field presents a potential solution to accelerate imaging of large samples when compression of information along the optical axis is not detrimental to the interpretation of images. We have implemented an extended depth of field (EDF) approach in a random illumination microscope (RIM).

Optimal trade-off filters for compressed Raman classification and spectrum reconstruction.

Compressed Raman spectroscopy is a promising technique for fast chemical analysis. In particular, classification between species with known spectra can be performed with measures acquired through a few binary filters. Moreover, it is possible to reconstruct spectra by using enough filters.

Optimizing sampling for surface localization in 3D-scanning microscopy.

3D-scanning fluorescence imaging of living tissue is in demand for less phototoxic acquisition process. For the imaging of biological surfaces, adaptive and sparse scanning schemes have been proven to efficiently reduce the light dose by concentrating acquisitions around the surface. In this paper, we focus on optimizing the scanning scheme at a constant photon budget, when the problem is to estimate the position of a biological surface whose intensity profile is modeled as a Gaussian shape.

Compressed Raman method combining classification and estimation of spectra with optimized binary filters.

Compressed Raman methods allow classification between known chemical species with only a few measurements through binary filters. We propose a methodology for binary filter optimization, in which filters are modified at each pixel so that classification can still be achieved pixel by pixel with a few measurements acquired in parallel, while retaining the ability to reconstruct a full spectrum when combining measurements from several pixels. This approach is robust to intensity variations between pixels.

An adaptive microscope for the imaging of biological surfaces.

Scanning fluorescence microscopes are now able to image large biological samples at high spatial and temporal resolution. This comes at the expense of an increased light dose which is detrimental to fluorophore stability and cell physiology. To highly reduce the light dose, we designed an adaptive scanning fluorescence microscope with a scanning scheme optimized for the unsupervised imaging of cell sheets, which underly the shape of many embryos and organs.

Compressed Raman classification method with upper-bounded error probability.

Classification of different species with Raman measurements is analyzed when a total of exactly $ N $N photons are detected with binary filtered Raman spectra instead of fixing the measuring time. The optimal classification method for this problem leads to classification error probabilities upper-bounded by the Bhattacharyya bound and that are invariant to the multiplication of the spectrum intensities by an unknown factor. Furthermore, it is shown that this approach can be implemented with a number of binary filters smaller than the number of species to discriminate.

Bhattacharyya bound for Raman spectrum classification with a couple of binary filters.

Bhattacharyya bounds of classification error probability between two species with Raman and binary compressed Raman measurements limited by Poisson photon noise are analyzed. They exhibit the relevant physical parameters and lead to a simple expression of a minimal number of photons necessary to upper bound the optimal classification error probability.

Precision of proportion estimation with binary compressed Raman spectrum.

The precision of proportion estimation with binary filtering of a Raman spectrum mixture is analyzed when the number of binary filters is equal to the number of present species and when the measurements are corrupted with Poisson photon noise. It is shown that the Cramer-Rao bound provides a useful methodology to analyze the performance of such an approach, in particular when the binary filters are orthogonal. It is demonstrated that a simple linear mean square error estimation method is efficient (i.

Programmable single-pixel-based broadband stimulated Raman scattering.

We report a simple add-on for broadband stimulated Raman scattering (SRS) microscopes to enable fast and programmable spectroscopy acquisition. It comprises a conventional dispersive spectrometer layout incorporating a fast digital micromirror device (DMD). The approach is validated by acquiring SRS spectra of standard chemicals.

Detection of imprecise estimations for polarization-resolved second-harmonic generation microscopy.

Second-harmonic generation microscopy can provide estimation of some local molecule distribution properties. However, in order not to get erroneous conclusions, it is important to detect measurements with insufficient precision. Such a detection technique is developed considering an approximation of the ultimate precision provided by the Cramer-Rao bound.

Comparison of different active polarimetric imaging modes for target detection in outdoor environment.

We address the detection of manufactured objects in different types of environments with active polarimetric imaging. Using an original, fully adaptive imager, we compare several imaging modes having different numbers of polarimetric degrees of freedom. We demonstrate the efficiency of active polarimetric imaging for decamouflage and hazardous object detection, and underline the characteristics that a polarimetric imager aimed at this type of application should possess.

Living cell dry mass measurement using quantitative phase imaging with quadriwave lateral shearing interferometry: an accuracy and sensitivity discussion.

Single-cell dry mass measurement is used in biology to follow cell cycle, to address effects of drugs, or to investigate cell metabolism. Quantitative phase imaging technique with quadriwave lateral shearing interferometry (QWLSI) allows measuring cell dry mass. The technique is very simple to set up, as it is integrated in a camera-like instrument.

Active polarimetric imager with near infrared laser illumination for adaptive contrast optimization.

We designed and built an active polarimetric imager with laser illumination at 1.5 μm wavelength for adaptive polarimetric contrast optimization. It can generate and analyze any polarization state on the Poincaré sphere in order to best adapt to the polarimetric properties of the scene.

Joint contrast optimization and object segmentation in active polarimetric images.

We present a method for automatic target detection based on the iterative interplay between an active polarimetric imager with adaptive capabilities and a snake-based image segmentation algorithm. It successfully addresses the difficult situations where the target and the background differ only by their polarimetric properties. This method illustrates the benefits of integrating digital processing algorithms at the heart of the image acquisition process rather than using them only for postprocessing.

Color image simulation for underwater optics.

Underwater optical image simulation is a valuable tool for oceanic science, especially for the characterization of image processing techniques such as color restoration. In this context, simulating images with a correct color rendering is crucial. This paper presents an extension of existing image simulation models to RGB imaging.

Influence of polarization filtering on image registration precision in underwater conditions.

Underwater images often suffer from poor visibility due to photon scattering. However, in some cases, optical polarization filtering techniques can decrease the contribution of the scattered light and improve the visual image quality. In this Letter, the influence of these techniques for underwater image registration is analyzed, particularly when backscattered light is the main perturbation induced by the submarine environment.

Mixed segmentation-detection-based technique for point target detection in nonhomogeneous sky.

This paper deals with point target detection in infrared images of the sky for which there are local variations of the gray level mean value. We show that considering a simple image model with the gray level mean value varying as a linear or a quadratic function of the pixel coordinates can improve mixed segmentation-detection performance in comparison to homogeneous model-based approaches.

Stochastic complexity integral image based technique for fast video tracking.

We propose a new method based on the minimization of the stochastic complexity for fast and efficient tracking adapted to video images with a static camera. The obtained criterion combines the advantages of background-subtraction-based techniques and those of using measures of similarities to a target model without requiring any tuning of a weighting parameter. It is then demonstrated that this approach can be implemented with a fast integral image technique to estimate the location and the rectangular shape of the target in a few milliseconds.

Minimum description length synthetic aperture radar image segmentation.

We present a new minimum description length (MDL) approach based on a deformable partition--a polygonal grid--for automatic segmentation of a speckled image composed of several homogeneous regions. The image segmentation thus consists in the estimation of the polygonal grid, or, more precisely, its number of regions, its number of nodes and the location of its nodes. These estimations are performed by minimizing a unique MDL criterion which takes into account the probabilistic properties of speckle fluctuations and a measure of the stochastic complexity of the polygonal grid.

Information-theory-based snake adapted to inhomogeneous intensity variations.

A new snake-based segmentation technique of a single object (simply connected) in the presence of inhomogeneous Gaussian noise is proposed, in which the mean in each region is modeled as a polynomial function of the coordinates and which is thus adapted to inhomogeneous illumination. It is shown that the minimization of the stochastic complexity of the image, which can be implemented efficiently, allows one to automatically estimate not only the number and the position of the nodes of the polygonal contour used to describe the object but also the degree of the polynomials that model the variations of the mean.

Minimal stochastic complexity image partitioning with unknown noise model.

We present a generalization of a new statistical technique of image partitioning into homogeneous regions to cases where the family of the probability laws of the gray-level fluctuations is a priori unknown. For that purpose, the probability laws are described with step functions whose parameters are estimated. This approach is based on a polygonal grid which can have an arbitrary topology and whose number of regions and regularity of its boundaries are obtained by minimizing the stochastic complexity of the image.

Nonparametric statistical snake based on the minimum stochastic complexity.

We propose a nonparametric statistical snake technique that is based on the minimization of the stochastic complexity (minimum description length principle). The probability distributions of the gray levels in the different regions of the image are described with step functions with parameters that are estimated. The segmentation is thus obtained by minimizing a criterion that does not include any parameter to be tuned by the user.

Minimal stochastic complexity snake-based technique adapted to an unknown noise model.

We propose a polygonal snake segmentation technique adapted to objects that can be composed of several regions with gray-level fluctuations described by a priori unknown probability laws. This approach is based on a histogram equalization and on the minimization of a criterion without parameter to be tuned by the user. We demonstrate the efficiency of this approach, which has low computational cost, on synthetic and real images perturbed by different types of optical noise.

Information-theory-based snake adapted to multiregion objects with different noise models.

We propose a segmentation technique adapted to objects composed of several regions with gray-level fluctuations described by different probability laws. This approach is based on information theory techniques and leads to a multiregion polygonal snake driven by the minimization of a criterion without any parameters to be tuned by the user. We demonstrate the improvements obtained with this approach as well as its low computational cost.