Resumen:
Computer vision is a scientific discipline that has been developed in recent decades due to
technological advances in acquisition devices together with the increase on computational
capabilities. The reduction in prices of hardware, both acquisition and processing, allows
this technology to be available to most users. Additionally, there is a technological advance
that allows sensors to be sensitive to different spectra, including smart mobile devices. Computer
vision is defined as a field of study that develops multiple techniques to ensure that
machines can "see" and "understand" information in images or videos of any spectrum, using
mathematical models that process, analyze and interpret digital information extracted from
images.
With the advance of convolutional neural networks (CNN), the usage of machine learning
based techniques has made great progress in recent years. Specifically, many techniques have
been developed to implement a process similar to the visual reasoning of human vision, to
performtasks such as detection, recognition, segmentation, coloring, filtering, improvement,
similarity, etc. using CNN. This thesis presents a series of CNN-based techniques applied
to images of different spectra, especially the near infrared spectrum (NIR) and the visible
spectrum. Among the techniques implemented are: perform similarity detection between
images of VISIBLE and NIR spectra, colorization of NIR images, estimation of normalized
difference vegetation index (NDVI) using only one band of the spectrum and eliminate the
haze present in the images. It should be noted that to implement these techniques, generative
adversarial models have been used in their standard, conditional, stacked and cyclic variants,
which are the latest generation in these type of networks.
