Image super resolution using compressed sensing observations
Metadatos
Mostrar el registro completo del ítemEditorial
Universidad de Granada
Departamento
Universidad de Granada. Departamento de Ciencias de la Computación e Inteligencia ArtificialMateria
Imágenes Tratamiento digital de imágenes Resolución (Optica) Color Algoritmos Procesado de imágenes Compresión
Materia UDC
77 070 (043.2) 120309
Fecha
2016Fecha lectura
2016-05-25Referencia bibliográfica
Alsaafin, W.H.A. Image super resolution using compressed sensing observations. Granada: Universidad de Granada, 2016. [http://hdl.handle.net/10481/43889]
Patrocinador
Tesis Univ. Granada. Programa Oficial de Doctorado en: Tecnologías de la Información y la ComunicaciónResumen
Compressed Sensing (CS) is a new technology that simultaneously acquires and compresses
images reducing acquisition time and memory requirements to process or transmit them. It es-
tablishes that a sparsely representable image/signal can be recovered from a highly incomplete
set of measurements or projections of the image. Image Super Resolution (SR) is an important
post-processing technique where multiple input images are super resolved to obtain one or
more images of higher resolution and better quality. SR necessitates a good image registration
procedure in order to obtain a High Resolution (HR) image of enhanced quality. Such quality
should overcome image degradation due to system hardware, optical and spatial limitations.
In this dissertation we propose a novel framework to obtain HR images from CS imaging
systems capturing multiple Low Resolution (LR) images of the same scene. The assumption
that when an image admits a sparse representation in a transformed domain, a blurred version
of it will also be sparse in the transformed domain allows us to recover blurred images from CS
observations Similarly, a warped, blurred, and down-sampled LR image is expected to be also
sparse in a transformed domain and hence can be reconstructed from the corresponding CS
observation. The proposed Compressed Sensing Super Resolution (CSSR) approach, combines
existing CS reconstruction algorithms with an LR to HR approach based on the use of a new
robust sparsity promoting prior based on super Gaussian regularization.