Principal-component decomposition is applied to the analysis of noise for infrared images. It provides a set of eigenimages, the principal components, that represents spatial patterns associated with different types of noise. We provide a method to classify the principal components into processes that explain a given amount of the variance of the images under analysis. Each process can reconstruct the set of data, thus allowing a calculation of the weight of the given process in the total noise. The method is successfully applied to an actual set of infrared images. The extension of the method to images in the visible spectrum is possible and would provide similar results.

Antenna-coupled optical detectors, also named optical antennas, are being developed and proposed as alternative detection devices for the millimetre, infrared, and visible spectra. Optical and infrared antennas represent a class of optical components that couple electromagnetic radiation in the visible and infrared wavelengths in the same way as radioelectric antennas do at the corresponding wavelengths. The size of optical antennas is in the range of the detected wavelength and they involve fabrication techniques with nanoscale spatial resolution. Optical antennas have already proved and potential advantages in the detection of light showing polarization dependence, tuneability, and rapid time response. They also can be considered as point detectors and directionally sensitive elements. So far, these detectors have been thoroughly tested in the mid-infrared with some positive results in the visible. The measurement and characterization of optical antennas requires the use of an experimental setup with nanometric resolution. On the other hand, a computation simulation of the interaction between the material structures and the incoming electromagnetic radiation is needed to explore alternative designs of practical devices.

No abstract

Bad pixels are defined as those pixels showing a temporal evolution of the signal different from the rest of the pixels of a given array. Principal component analysis helps us to understand the definition of a statistical distance associated with each pixels, and using this distance it is possible to identify those pixels labeled as bad pixels. The spatiality of a pixel is also calculated. An assumption about the normality of the distribution of the distances of the pixels is revised. Although the influence on the robustness of the identification algorithm is negligible, the definition of a parameter related with this nonnormality helps to identify those principal components and eigenimages responsible for the departure from a multinormal distribution. The method for identifying the bad pixels is successfully applied to a set of frames obtained from a CCD visible and a focal plane array (FPA) IR camera.

Meanderline wave plates are in common use at radio frequencies as polarization retarders. We present initial results of a gold meanderline structure on a silicon substrate that functions at a wavelength of 10.6 microm in the IR. The measured results show a distinct change in the polarization state of the incident beam after passing through the device, inducing a 74 degrees phase retardance between horizontal and vertical components. A high degree of polarization (88%) is maintained in the transmitted beam with an overall power transmittance of 38% and a beam profile that remains essentially unchanged.