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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.

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We address the data processing methods needed for imaging with a long baseline optical interferometer. We first describe parametric reconstruction approaches and adopt a general formulation of nonparametric image reconstruction as the solution of a constrained optimization problem. Within this framework, we present two recent reconstruction methods, MIRA and WISARD, representative of the two generic approaches for dealing with the missing phase information. MIRA is based on an implicit approach and a direct optimization of a Bayesian criterion while WISARD adopts a self-calibration approach and an alternate minimization scheme inspired from radio-astronomy. Both methods can handle various regularization criteria. We review commonly used regularization terms and introduce an original quadratic regularization called "soft support constraint" that favors the object compactness. It yields images of quality comparable to nonquadratic regularizations on the synthetic data we have processed. We then perform image reconstructions, both parametric and nonparametric, on astronomical data from the IOTA interferometer, and discuss the respective roles of parametric and nonparametric approaches for optical interferometric imaging.

Aims.We undertook an H band interferometric examination of Arcturus, a star frequently used as a spatial and spectral calibrator. Methods. Using the IOTA 3 telescope interferometer, we performed spectro-interferometric observations (R ≈ 35) of Arcturus. Atmospheric models and prescriptions were fitted to the data to derive the brightness distribution of the photosphere. Image reconstruction was performed using two software algorithms: Wisard and Mira.Results. An achromatic power law proved to be a good model of the brightness distribution, with a limb darkening compatible with the one derived from atmospheric model simulations using our marcs model. A Rosseland diameter of 21.05 ± 0.21 was derived, corresponding to an effective temperature of T eff = 4295 ± 26 K. No companion was detected from the closure phases, with an upper limit on the brightness ratio of 8 × 10 −4 at 1 AU. The dynamic range at such distance from the photosphere was established as 1.5 × 10 −4 (1σ rms). An upper limit of 1.7 × 10 −3 was also derived for the level of brightness asymmetries present in the photosphere.

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International audienceWe present a new approach for spatially varying blur identification using a single image. Within each local patch in the image, the local blur is selected between a finite set of candidate PSFs by a maximum likelihood approach. We propose to work with a Generalized Likelihood to reduce the number of parameters and we use the Generalized Singular Value De- composition to limit the computing cost, while making proper image boundary hypotheses. The resulting method is fast and demonstrates good performance on simulated and real examples originating from applications such as motion blur identification and depth from defocus

Entropy-based methods are widely used for solving inverse problems, particularly when the solution is known to be positive. Here, we address linear ill-posed and noisy inverse problems of the form z = Ax + n z = Ax + n z = Ax + n with a general convex constraint x 2 X x 2 X x 2 X, where X X X is a convex set. Although projective methods are well adapted to this context, we study alternative methods which rely highly on some "information-based" criteria. Our goal is to clarify the role played by entropy in this field, and to present a new point of view on entropy, using general tools and results coming from convex analysis. We present then a new and broad scheme for entropic-based inversion of linear-noisy inverse problems. This scheme was introduced by Navaza in 1985 in connection with a physical modeling for crystallographic applications, and further studied by Dacunha-Castelle and Gamboa. Important features of this paper are: i) a unified presentation of many well-known reconstruction criteria, ii) proposal of new criteria for reconstruction under various prior knowledge and with various noise statistics, iii) a description of practical inversion of data using the aforementioned criteria, and iv) a presentation of some reconstruction results.