1,706 research outputs found
Exploiting Image Local And Nonlocal Consistency For Mixed Gaussian-Impulse Noise Removal
Most existing image denoising algorithms can only deal with a single type of
noise, which violates the fact that the noisy observed images in practice are
often suffered from more than one type of noise during the process of
acquisition and transmission. In this paper, we propose a new variational
algorithm for mixed Gaussian-impulse noise removal by exploiting image local
consistency and nonlocal consistency simultaneously. Specifically, the local
consistency is measured by a hyper-Laplace prior, enforcing the local
smoothness of images, while the nonlocal consistency is measured by
three-dimensional sparsity of similar blocks, enforcing the nonlocal
self-similarity of natural images. Moreover, a Split-Bregman based technique is
developed to solve the above optimization problem efficiently. Extensive
experiments for mixed Gaussian plus impulse noise show that significant
performance improvements over the current state-of-the-art schemes have been
achieved, which substantiates the effectiveness of the proposed algorithm.Comment: 6 pages, 4 figures, 3 tables, to be published at IEEE Int. Conf. on
Multimedia & Expo (ICME) 201
An optimal subgradient algorithm for large-scale convex optimization in simple domains
This paper shows that the optimal subgradient algorithm, OSGA, proposed in
\cite{NeuO} can be used for solving structured large-scale convex constrained
optimization problems. Only first-order information is required, and the
optimal complexity bounds for both smooth and nonsmooth problems are attained.
More specifically, we consider two classes of problems: (i) a convex objective
with a simple closed convex domain, where the orthogonal projection on this
feasible domain is efficiently available; (ii) a convex objective with a simple
convex functional constraint. If we equip OSGA with an appropriate
prox-function, the OSGA subproblem can be solved either in a closed form or by
a simple iterative scheme, which is especially important for large-scale
problems. We report numerical results for some applications to show the
efficiency of the proposed scheme. A software package implementing OSGA for
above domains is available
Mammographic image restoration using maximum entropy deconvolution
An image restoration approach based on a Bayesian maximum entropy method
(MEM) has been applied to a radiological image deconvolution problem, that of
reduction of geometric blurring in magnification mammography. The aim of the
work is to demonstrate an improvement in image spatial resolution in realistic
noisy radiological images with no associated penalty in terms of reduction in
the signal-to-noise ratio perceived by the observer. Images of the TORMAM
mammographic image quality phantom were recorded using the standard
magnification settings of 1.8 magnification/fine focus and also at 1.8
magnification/broad focus and 3.0 magnification/fine focus; the latter two
arrangements would normally give rise to unacceptable geometric blurring.
Measured point-spread functions were used in conjunction with the MEM image
processing to de-blur these images. The results are presented as comparative
images of phantom test features and as observer scores for the raw and
processed images. Visualization of high resolution features and the total image
scores for the test phantom were improved by the application of the MEM
processing. It is argued that this successful demonstration of image
de-blurring in noisy radiological images offers the possibility of weakening
the link between focal spot size and geometric blurring in radiology, thus
opening up new approaches to system optimization.Comment: 18 pages, 10 figure
Deblurring by Solving a TV p
Image deblurring is formulated as an unconstrained minimization problem, and its penalty function is the sum of the error term and TVp-regularizers
with 0<p<1. Although TVp-regularizer is a powerful tool that can significantly promote the sparseness of image gradients, it is neither convex nor smooth, thus making the
presented optimization problem more difficult to deal with. To solve this minimization
problem efficiently, such problem is first reformulated as an equivalent constrained minimization problem by introducing new variables and new constraints. Thereafter, the split
Bregman method, as a solver, splits the new constrained minimization problem into subproblems. For each subproblem, the corresponding efficient method is applied to ensure
the existence of closed-form solutions. In simulated experiments, the proposed algorithm
and some state-of-the-art algorithms are applied to restore three types of blurred-noisy
images. The restored results show that the proposed algorithm is valid for image deblurring and is found to outperform other algorithms in experiments
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
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