| Summary: | Camera producers try to increase the spatial resolution of a camera by reducing
size of sites on sensor array. However, shot noise causes the signal to noise ratio drop as
sensor sites get smaller. This fact motivates resolution enhancement to be performed
through software. Super resolution (SR) image reconstruction aims to combine degraded
images of a scene in order to form an image which has higher resolution than
all observations. There is a demand for high resolution images in biomedical imaging,
surveillance, aerial/satellite imaging and high-definition TV (HDTV) technology. Although
extensive research has been conducted in SR, attention has not been given to
increase the resolution of images under illumination changes. In this study, a unique
framework is proposed to increase the spatial resolution and dynamic range of a video
sequence using Bayesian and Projection onto Convex Sets (POCS) methods. Incorporating
camera response function estimation into image reconstruction allows dynamic
range enhancement along with spatial resolution improvement. Photometrically varying
input images complicate process of projecting observations onto common grid by
violating brightness constancy. A contrast invariant feature transform is proposed in
this thesis to register input images with high illumination variation. Proposed algorithm
increases the repeatability rate of detected features among frames of a video.
Repeatability rate is increased by computing the autocorrelation matrix using the
gradients of contrast stretched input images. Presented contrast invariant feature detection
improves repeatability rate of Harris corner detector around %25 on average.
Joint multi-frame demosaicking and resolution enhancement is also investigated in
this thesis. Color constancy constraint set is devised and incorporated into POCS
framework for increasing resolution of color-filter array sampled images. Proposed
method provides fewer demosaicking artifacts compared to existing POCS method
and a higher visual quality in final image.
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