We highlight the interest of using the indices of polarimetric purity (IPPs) to the inspection of biological tissues. The IPPs were recently proposed in the literature and they result in a further synthetization of the depolarizing properties of samples. Compared with standard polarimetric images of biological samples, IPP-based images lead to larger image contrast of some biological structures and to a further physical interpretation of the depolarizing mechanisms inherent to the samples. In addition, unlike other methods, their calculation do not require advanced algebraic operations (as is the case of polar decompositions), and they result in 3 indicators of easy implementation. We also propose a pseudo-colored encoding of the IPP information that leads to an improved visualization of samples. This last technique opens the possibility of tailored adjustment of tissues contrast by using customized pseudo-colored images. The potential of the IPP approach is experimentally highlighted along the manuscript by studying 3 different ex-vivo samples. A significant image contrast enhancement is obtained by using the IPP-based methods, compared to standard polarimetric images.

We present an intuitive and versatile method that can dynamically generate 2D and 3D tailored light patterns. The light structures are generated by dynamically implementing discrete and continuous split lens configurations onto a spatial light modulator. These configurations can be dynamically modified by tuning a reduced number of control parameters with simple physical interpretation. We demonstrate the versatility of the method by experimentally implementing a wide number of structured beams, including optical lattices, a light cone, and vortex beams carrying orbital angular momentum. Compared with other optical illuminators, the advantages of our method are its simple interpretation and control for creating the light structures, and that it is based on a robust, dynamic and easy-to-build optical set-up. The proposed method may be useful in a large number of applications, such as optical trapping, super-resolution imaging or illuminating arrays of photonic switching devices.

An optical setup able to generate arbitrary states of polarization (SOPs) with customized degree of polarization is presented in this Letter. Compared with the few alternatives existing in literature, it presents an easy-to-build optical setup and leads to a superior performance. In fact, experimental results are presented, providing an accurate control for the generation of SOPs (maximum error of 1.7% and 3.3% for ellipticity and azimuth, respectively) as well as for the associated degree of polarization (full experimental variation from 1 up to 0.003, with a 1.7% maximum error). The system proposed may be useful for different applications, for example, for polarimeters testing, speckle metrology, and biological applications.

Polarimetric data is nowadays used to build recognition models for the characterization of organic tissues or the early detection of some diseases. Different Mueller matrix‐derived polarimetric observables, which allow a physical interpretation of a specific characteristic of samples, are proposed in literature to feed the required recognition algorithms. However, they are obtained through mathematical transformations of the Mueller matrix and this process may loss relevant sample information in search of physical interpretation. In this work, we present a thorough comparative between 12 classification models based on different polarimetric datasets to find the ideal polarimetric framework to construct tissues classification models. The study is conducted on the experimental Mueller matrices images measured on different tissues: muscle, tendon, myotendinous junction and bone; from a collection of 165 ex‐vivo chicken thighs. Three polarimetric datasets are analyzed: (A) a selection of most representative metrics presented in literature; (B) Mueller matrix elements; and (C) the combination of (A) and (B) sets. Results highlight the importance of using raw Mueller matrix elements for the design of classification models.