223 research outputs found
Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models
Segmentation is a fundamental task for extracting semantically meaningful
regions from an image. The goal of segmentation algorithms is to accurately
assign object labels to each image location. However, image-noise, shortcomings
of algorithms, and image ambiguities cause uncertainty in label assignment.
Estimating the uncertainty in label assignment is important in multiple
application domains, such as segmenting tumors from medical images for
radiation treatment planning. One way to estimate these uncertainties is
through the computation of posteriors of Bayesian models, which is
computationally prohibitive for many practical applications. On the other hand,
most computationally efficient methods fail to estimate label uncertainty. We
therefore propose in this paper the Active Mean Fields (AMF) approach, a
technique based on Bayesian modeling that uses a mean-field approximation to
efficiently compute a segmentation and its corresponding uncertainty. Based on
a variational formulation, the resulting convex model combines any
label-likelihood measure with a prior on the length of the segmentation
boundary. A specific implementation of that model is the Chan-Vese segmentation
model (CV), in which the binary segmentation task is defined by a Gaussian
likelihood and a prior regularizing the length of the segmentation boundary.
Furthermore, the Euler-Lagrange equations derived from the AMF model are
equivalent to those of the popular Rudin-Osher-Fatemi (ROF) model for image
denoising. Solutions to the AMF model can thus be implemented by directly
utilizing highly-efficient ROF solvers on log-likelihood ratio fields. We
qualitatively assess the approach on synthetic data as well as on real natural
and medical images. For a quantitative evaluation, we apply our approach to the
icgbench dataset
A Novel Euler's Elastica based Segmentation Approach for Noisy Images via using the Progressive Hedging Algorithm
Euler's Elastica based unsupervised segmentation models have strong
capability of completing the missing boundaries for existing objects in a clean
image, but they are not working well for noisy images. This paper aims to
establish a Euler's Elastica based approach that properly deals with random
noises to improve the segmentation performance for noisy images. We solve the
corresponding optimization problem via using the progressive hedging algorithm
(PHA) with a step length suggested by the alternating direction method of
multipliers (ADMM). Technically, all the simplified convex versions of the
subproblems derived from the major framework of PHA can be obtained by using
the curvature weighted approach and the convex relaxation method. Then an
alternating optimization strategy is applied with the merits of using some
powerful accelerating techniques including the fast Fourier transform (FFT) and
generalized soft threshold formulas. Extensive experiments have been conducted
on both synthetic and real images, which validated some significant gains of
the proposed segmentation models and demonstrated the advantages of the
developed algorithm
Total variation regularization for manifold-valued data
We consider total variation minimization for manifold valued data. We propose
a cyclic proximal point algorithm and a parallel proximal point algorithm to
minimize TV functionals with -type data terms in the manifold case.
These algorithms are based on iterative geodesic averaging which makes them
easily applicable to a large class of data manifolds. As an application, we
consider denoising images which take their values in a manifold. We apply our
algorithms to diffusion tensor images, interferometric SAR images as well as
sphere and cylinder valued images. For the class of Cartan-Hadamard manifolds
(which includes the data space in diffusion tensor imaging) we show the
convergence of the proposed TV minimizing algorithms to a global minimizer
Harmonic mappings valued in the Wasserstein space
We propose a definition of the Dirichlet energy (which is roughly speaking
the integral of the square of the gradient) for mappings mu : Omega -> (P(D),
W\_2) defined over a subset Omega of R^p and valued in the space P(D) of
probability measures on a compact convex subset D of R^q endowed with the
quadratic Wasserstein distance. Our definition relies on a straightforward
generalization of the Benamou-Brenier formula (already introduced by Brenier)
but is also equivalent to the definition of Koorevaar, Schoen and Jost as limit
of approximate Dirichlet energies, and to the definition of Reshetnyak of
Sobolev spaces valued in metric spaces. We study harmonic mappings, i.e.
minimizers of the Dirichlet energy provided that the values on the boundary d
Omega are fixed. The notion of constant-speed geodesics in the Wasserstein
space is recovered by taking for Omega a segment of R. As the Wasserstein space
(P(D), W\_2) is positively curved in the sense of Alexandrov we cannot apply
the theory of Koorevaar, Schoen and Jost and we use instead arguments based on
optimal transport. We manage to get existence of harmonic mappings provided
that the boundary values are Lipschitz on d Omega, uniqueness is an open
question. If Omega is a segment of R, it is known that a curve valued in the
Wasserstein space P(D) can be seen as a superposition of curves valued in D. We
show that it is no longer the case in higher dimensions: a generic mapping
Omega -> P(D) cannot be represented as the superposition of mappings Omega ->
D. We are able to show the validity of a maximum principle: the composition
F(mu) of a function F : P(D) -> R convex along generalized geodesics and a
harmonic mapping mu : Omega -> P(D) is a subharmonic real-valued function. We
also study the special case where we restrict ourselves to a given family of
elliptically contoured distributions (a finite-dimensional and geodesically
convex submanifold of (P(D), W\_2) which generalizes the case of Gaussian
measures) and show that it boils down to harmonic mappings valued in the
Riemannian manifold of symmetric matrices endowed with the distance coming from
optimal transport
Function-valued Mappings and SSIM-based Optimization in Imaging
In a few words, this thesis is concerned with two alternative approaches to imag- ing, namely, Function-valued Mappings (FVMs) and Structural Similarity Index Measure (SSIM)-based Optimization. Briefly, a FVM is a mathematical object that assigns to each element in its domain a function that belongs to a given function space. The advantage of this representation is that the infinite dimensionality of the range of FVMs allows us to give a more accurate description of complex datasets such as hyperspectral images and diffusion magnetic resonance images, something that can not be done with the classical representation of such data sets as vector-valued functions. For instance, a hyperspectral image can be described as a FVM that assigns to each point in a spatial domain a spectral function that belongs to the function space L2(R); that is, the space of functions whose energy is finite. Moreoever, we present a Fourier transform and a new class of fractal transforms for FVMs to analyze and process hyperspectral images.
Regarding SSIM-based optimization, we introduce a general framework for solving op- timization problems that involve the SSIM as a fidelity measure. This framework offers the option of carrying out SSIM-based imaging tasks which are usually addressed using the classical Euclidean-based methods. In the literature, SSIM-based approaches have been proposed to address the limitations of Euclidean-based metrics as measures of vi- sual quality. These methods show better performance when compared to their Euclidean counterparts since the SSIM is a better model of the human visual system; however, these approaches tend to be developed for particular applications. With the general framework that it is presented in this thesis, rather than focusing on particular imaging tasks, we introduce a set of novel algorithms capable of carrying out a wide range of SSIM-based imaging applications. Moreover, such a framework allows us to include the SSIM as a fidelity term in optimization problems in which it had not been included before
A Spatially Adaptive Edge-Preserving Denoising Method Based on Fractional-Order Variational PDEs
Image denoising is a basic problem in image processing. An important task of image denoising is to preserve the significant geometric features such as edges and textures while filtering out noise. So far, this is still a problem to be further studied. In this paper, we firstly introduce an edge detection function based on the Gaussian filtering operator and then analyze the filtering characteristic of the fractional derivative operator. On the basis, we establish the spatially adaptive fractional edge-preserving denoising model in the variational framework, discuss the existence and uniqueness of our proposed model solution and derive the nonlinear fractional Euler-Lagrange equation for solving our proposed model. This forms a fractional order extension of the first and second order variational approaches. Finally, we apply the proposed method to the synthetic images and real seismic data denoising to verify the effectiveness of our method and compare the experimental results of our method with the related state-of-the-art methods. Experimental results illustrate that our proposed method can not only improve the signal to noise ratio (SNR) but also adaptively preserve the structural information of an image compared with other contrastive methods. Our proposed method can also be applied to remote sensing imaging, medical imaging and so onThe work of Dehua Wang was supported in part by the Science and Technology Planning Project of Shaanxi Province under Grant
2020JM-561, in part by the Postdoctoral Foundation of China under Grant 2019M663462, in part by the Innovative Talents Cultivate
Program of Shaanxi Province under Grant 2019KJXX-032, in part by the President Fund of Xi’an Technological University under Grant
XAGDXJJ17026, and in part by the Teaching Reform Project of Xi’an Technological University under Grant 18JGY08. The work of Juan
J. Nieto was supported in part by the Agencia Estatal de Investigacion (AEI) of Spain under Grant MTM2016-75140-P, and in part by the
European Community Fund FEDER. The work of Xiaoping Li was supported in part by the NSFC under Grant 61701086, and in part by
the Fundamental Research Funds for the Central Universities under Grant ZYGX2016KYQD143S
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