Interaction of TM-polarized waves with a subwavelength thick metallic slit has been analyzed. A Fabry-Pérot-like behavior is reported. The resonance peaks, however, have very low magnitude and a systematic shift towards longer wavelengths is observed. The slit being narrow, this shift can be interpreted as the result of an aperture effect. Spectral transmission from a periodic array of such slits features the same peaks with a high increase in their magnitude, confirming that a grating acts as an amplifier of those resonances. Such a mechanism might explain the enhancement of the transmission observed in recent experiments [T. W. Ebbesen, H. J. Lezec, H. F. Ghaemi, T. Thio, and P. A. Wolff, Nature (London) 391, 667 (1998)].

By focusing light with a sphere several wavelengths in diameter, we can obtain a photonic nanojet [Opt. Express 13, 526 (2005)]: if light is focused on the surface of the sphere, the width of the beam stays smaller than the wavelength along a distance of propagation of approximately two wavelengths and reaches a high intensity. We use the rigorous Mie theory to analyze the basic properties of the photonic jet in the general three-dimensional polarized case. This fast algorithm allows us to determine the influence of the radius and the refractive index of the sphere on the photonic jet. The polarization response is also studied. We observe that high-intensity concentrations and subwavelength focusing are two different effects. Their basic properties are analyzed, and explanations are proposed.

We present an imaging system that measures the polarimetric state of the light coming from each point of a scene. This system, which determines the four components of the Stokes vector at each spatial location, is based on a liquid-crystal polarization modulator, which makes it possible to acquire four-dimensional Stokes parameter images at a standard video rate. We show that using such polarimetric images instead of simple intensity images can improve target detection and segmentation performance.

This paper extends and refines previous work on clustering of polarization-encoded images. The polarization-encoded images used in this work are considered as multidimensional parametric images where a clustering scheme based on Markovian Bayesian inference is applied. Hidden Markov Chains Model (HMCM) and Hidden Hierarchical Markovian Model (HHMM) show to handle effectively Mueller images and give very good results for biological tissues (vegetal leaves). Pretreatments attempting to reduce the image dimensionality based on the Principal Component Analysis (PCA) turns out to be useless for Mueller matrix images.

Two methods used to retrieve Mueller matrices from intensity measurements are revisited. It is shown that with symmetry or orthogonality considerations, numerical inversions of polarimetric equations can be avoided. With the obtained analytical formulas, noise propagation can be analyzed. If the intensity noise is a Gaussian white noise, the noise of Mueller matrices features remarkable properties. Mueller components are mutually correlated according to a scheme that involves decomposition into four blocks of 2x2 matrices. Variances are unequally distributed: the middle 2x2 block has the highest variance, the element on the bottom right has the lowest. These characteristics have been validated on experimental Mueller images of the free space.