We give an overview of recent techniques which use a level set representation of shapes for solving inverse scattering problems. The main focus is on electromagnetic scattering using different popular models, as for example Maxwell's equations, TM-polarized and TE-polarized waves, impedance tomography, a transport equation or its diffusion approximation. These models also are representative for a broader class of inverse problems. Starting out from the original binary approach of Santosa for solving the corresponding shape reconstruction problem, we successively develop more recent generalizations, as for example using colour or vector level sets. Shape sensitivity analysis and topological derivatives are discussed as well in this framework. Moreover, various techniques for incorporating regularization into the shape inverse problem using level sets are demonstrated, which also include the choice of subclasses of simple shapes, such as ellipsoids, for the inversion. Finally, we present various numerical examples in 2D and in 3D for demonstrating the performance of level set techniques in realistic applications.

We are concerned with the retrieval of the unknown cross section of a homogeneous cylindrical obstacle embedded in a homogeneous medium and illuminated by time-harmonic electromagnetic line sources. The dielectric parameters of the obstacle and embedding materials are known and piecewise constant. That is, the shape (here, the contour) of the obstacle is sufficient for its full characterization. The inverse scattering problem is then to determine the contour from the knowledge of the scattered field measured for several locations of the sources and/or frequencies. An iterative process is implemented: given an initial contour, this contour is progressively evolved such as to minimize the residual in the data fit. This algorithm presents two main important points. The first concerns the choice of the transformation enforced on the contour. We will show that this involves the design of a velocity field whose expression only requires the resolution of an adjoint problem at each step. The second concerns the use of a level-set function in order to represent the obstacle. This level-set function will be of great use to handle in a natural way splitting or merging of obstacles along the iterative process. The evolution of this level-set is controlled by a Hamilton-Jacobi-type equation which will be solved by using an appropriate finite-difference scheme. Numerical results of inversion obtained from both noiseless and noisy synthetic data illustrate the behaviour of the algorithm for a variety of obstacles.

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The imaging of a thin inclusion, with dielectric and/or magnetic contrasts with respect to the embedding homogeneous medium, is investigated. A MUSIC-type algorithm operating at a single time-harmonic frequency is developed in order to map the inclusion (that is, to retrieve its supporting curve) from scattered field data collected within the multi-static response matrix. Numerical experiments carried out for several types of inclusions (dielectric and/or magnetic ones, straight or curved ones), mostly single inclusions and also two of them close by as a straightforward extension, illustrate the pros and cons of the proposed imaging method.

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We give an update on recent techniques which use a level set representation of shapes for solving inverse scattering problems, completing in that matter the exposition made in (Dorn and Lesselier 2006 Inverse Problems 22 R67) and (Dorn and Lesselier 2007 Deformable Models (New York: Springer) pp 61-90), and bringing it closer to the current state of the art.

We are concerned herein with inverse scattering problems in stratified media and aspect-limited data configurations. In such configurations, the sources and receivers of the probing waves are located in a medium different from the one which contains the object under test. This results in a lack of information which enhances the inherent ill-posedness of the inverse problem. To make the problem more tractable, we assume that the test object is homogeneous with known constitutive parameters so that the inverse problem consists of reconstructing its shape and location. This non-linear inverse problem is solved using the modified gradient method in which the a priori information is introduced as a binary constraint. A cooling parameter is introduced at the same time, which allows us to control the evolution of the iterative process. The effectiveness of this algorithm is studied for three different physical applications.