Feature-preserving image restoration and its application in biological fluorescence microscopy
Abstract
This thesis presents a new investigation of image restoration and its application to
fluorescence cell microscopy. The first part of the work is to develop advanced image
denoising algorithms to restore images from noisy observations by using a novel featurepreserving
diffusion approach. I have applied these algorithms to different types of
images, including biometric, biological and natural images, and demonstrated their
superior performance for noise removal and feature preservation, compared to several
state of the art methods. In the second part of my work, I explore a novel, simple and
inexpensive super-resolution restoration method for quantitative microscopy in cell
biology. In this method, a super-resolution image is restored, through an inverse process,
by using multiple diffraction-limited (low) resolution observations, which are acquired
from conventional microscopes whilst translating the sample parallel to the image plane,
so referred to as translation microscopy (TRAM). A key to this new development is the
integration of a robust feature detector, developed in the first part, to the inverse process
to restore high resolution images well above the diffraction limit in the presence of strong
noise. TRAM is a post-image acquisition computational method and can be implemented
with any microscope. Experiments show a nearly 7-fold increase in lateral spatial
resolution in noisy biological environments, delivering multi-colour image resolution of
~30 nm.