Resumen de Multidimensional Computational Imaging using Single-pixel Detectors
- español
El ser humano depende de su sentido de la vista para obtener información de sus alrededores. Sin embargo, este está claramente limitado: somos incapaces de ver objetos muy lejanos o pequeños. Para afrontar estas limitaciones hemos desarrollado multitud de sistemas ópticos. Sin embargo, hoy en día aún quedan fronteras físicas que nuestros dispositivos no son capaces de atravesar. En esta tesis veremos cómo es posible obtener imágenes con información sobre múltiples dimensiones de la luz (polarización, fase, longitud de onda) utilizando sistemas basados en detectores de un solo píxel, moduladores espaciales de luz y técnicas computacionales de procesado de señal. De este modo, es posible construir sistemas ópticos que miden mayores cantidades de información que los sistemas tradicionales mientras se reduce el coste económico de los mismos y se aumenta su velocidad.
- English
Humans depend on vision to gather information about their surroundings. However, our sight sense is very limited: we cannot see very distant or small objects, and we can only sense light inside the visible spectra. To tackle these limitations, we have been developing optical sensing tools for more than four centuries now. However, even though nowadays we can even see objects at the nanometric scale, or very distant galaxies, there are still fundamental limitations that physical systems cannot bypass.
In this thesis, I will show you how to obtain images with information about multiple dimensions of light (polarization, phase, wavelength) using novel sensing paradigms based on single-pixel detection and signal processing techniques. Using detectors without spatial resolution makes it possible to easily work in exotic spectral ranges, in low light level scenarios, or to build very compact and efficient multidimensional imaging systems. Moreover, the presence of a fast spatial light modulator in all of these systems allows to implement modern recovery techniques based on algorithmic approaches, such as compressive sensing or matrix completion. In doing so, these computational imaging systems can obtain more information than a traditional system, but in a faster and inexpensive manner.
