Title page for ETD etd-12202008-115800

Type of Document Dissertation
Author Gevrekci, Lutfi Murat
Author's Email Address lgevre1@lsu.edu, gevrekci_murat@yahoo.com
URN etd-12202008-115800
Title Super Resolution and Dynamic Range Enhancement of Image Sequences
Degree Doctor of Philosophy (Ph.D.)
Department Electrical & Computer Engineering
Advisory Committee
Advisor Name Title
Bahadir Kursat Gunturk Committee Chair
Jerry Trahan Committee Member
Jianhua Chen Committee Member
suresh rai Committee Member
Guoping Zhang Dean's Representative
  • demosaicking
  • super resolution
  • dynamic range enhancement
  • salient point detection
Date of Defense 2008-10-24
Availability unrestricted
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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