1,433 research outputs found
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
Feature-preserving image restoration and its application in biological fluorescence microscopy
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
Parameter optimization for local polynomial approximation based intersection confidence interval filter using genetic algorithm: an application for brain MRI image de-noising
Magnetic resonance imaging (MRI) is extensively exploited for more accuratepathological changes as well as diagnosis. Conversely, MRI suffers from variousshortcomings such as ambient noise from the environment, acquisition noise from theequipment, the presence of background tissue, breathing motion, body fat, etc.Consequently, noise reduction is critical as diverse types of the generated noise limit the efficiency of the medical image diagnosis. Local polynomial approximation basedintersection confidence interval (LPA-ICI) filter is one of the effective de-noising filters.This filter requires an adjustment of the ICI parameters for efficient window size selection.From the wide range of ICI parametric values, finding out the best set of tunes values is itselfan optimization problem. The present study proposed a novel technique for parameteroptimization of LPA-ICI filter using genetic algorithm (GA) for brain MR imagesde-noising. The experimental results proved that the proposed method outperforms theLPA-ICI method for de-noising in terms of various performance metrics for different noisevariance levels. Obtained results reports that the ICI parameter values depend on the noisevariance and the concerned under test image
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