456 research outputs found
A scaled, inexact and adaptive Fast Iterative Soft-Thresholding Algorithm for convex image restoration
No full textInternational audienceIn this note, we consider a special instance of the scaled, inexact and adaptive generalised Fast Iterative Soft-Thresholding Algorithm (SAGE-FISTA) recently proposed in (Rebegoldi, Calatroni, '21) for the efficient solution of strongly convex composite optimisation problems. In particular, we address here the sole (non-strongly) convex optimisation scenario, which is frequently encountered in many imaging applications. The proposed inexact S-FISTA algorithm shows analogies to the variable metric and inexact version of FISTA studied in (Bonettini, Rebegoldi, Ruggiero, '19), the main difference being the use of an adaptive (non-monotone) backtracking strategy allowing for the automatic adjustment of the algorithmic step-size along the iterations (see (Scheinberg, Goldfarb, Bai, '14, Calatroni, Chambolle, '19)). A quadratic convergence result in function values depending on the backtracking parameters and the upper and lower bounds on the spectrum of the variable metric operators is given. Experimental results on TV image deblurring problems with Poisson noise are then reported for numerical validation, showing improved computational efficiency and precision
Simple model for the rf field amplitude dependence of the trapped flux sensitivity in superconducting rf cavities
No full textThe improvement of the performance of superconducting rf cavities has recently motivated a considerable research effort in order to elucidate the effect of trapped magnetic flux on the surface resistance R_{s}. In this paper, we show that, by introducing a nonlinear pinning force in the Gittleman-Rosenblum equations for the rf power dissipation due to a trapped magnetic flux in a superconductor, we can empirically describe the linear dependence on the rf field amplitude B_{rf0} of the additional surface resistance R_{fl}. We also show that the proportionality between the rf-field-dependent and -independent terms R_{fl}^{1} and R_{fl}^{0} and the frequency dependence of R_{fl}^{1} follow naturally from this approach
Advances in the study of HTS superconductors for the beam impedance mitigation in CERN-FCC: the thermal runaway problem
No full textIn CERN Future Circular Collider (FCC-hh), a possible next-generation high-energy hadron–hadron collider, the center-of-mass collision energy will be of 100 TeV, with opposite proton beams of 50 TeV steered in a 100-km circumference tunnel by 16 T superconducting magnets. The synchrotron radiation, emitted by the beams, is absorbed by a beam-facing screen held at 50 K. The surface impedance of this screen has a strong impact on the beam stability, and copper at 50 K allows only tight beam stability margin. This has motivated investigating the possibility of high-temperature superconductors (HTSs) coatings on the beam screen internal surface, as a possible solution. In this communication, we will briefly review the general theory of the surface resistance of HTS in high field, low frequency regimes and will present specific calculations for REBCO commercial tapes that represent one of the possible envisaged solutions. The possible “thermal runaway” problems arising using REBCO tapes are then discussed. In particular, the upper limit for the transverse thermal resistance that guarantees thermal stability is quantitatively determined as a function of the REBCO superconducting properties at FCC operating conditions
Experience with the Production and Testing of the Prototypes of LHC Collimators
No full textCERN TS Technical Not
Stochastic Gradient Descent for Linear Inverse Problems in Variable Exponent Lebesgue Spaces
No full textWe consider a stochastic gradient descent (SGD) algorithm for solving linear inverse problems (e.g., CT image reconstruction) in the Banach space framework of variable exponent Lebesgue spaces ppnq pRq. Such non-standard spaces have been recently proved to be the appropriate functional framework to enforce pixel-adaptive regularisation in signal and image processing applications. Compared to its use in Hilbert settings, however, the application of SGD in the Banach setting of ppnq pRq is not straightforward, due, in particular to the lack of a closed-form expression and the non-separability property of the underlying norm. In this manuscript, we show that SGD iterations can effectively be performed using the associated modular function. Numerical validation on both simulated and real CT data show significant improvements in comparison to SGD solutions both in Hilbert and other Banach settings, in particular when non-Gaussian or mixed noise is observed in the data
Scaled, inexact and adaptive generalized FISTA for strongly convex optimization
No full textInternational audienceWe consider a variable metric and inexact version of the FISTA-type algorithm considered in (Chambolle, Pock, 2016, Calatroni, Chambolle, 2019) for the minimization of the sum of two (possibly strongly) convex functions. The proposed algorithm is combined with an adaptive (non-monotone) backtracking strategy, which allows for the adjustment of the algorithmic step-size along the iterations in order to improve the convergence speed. We prove a linear convergence result for the function values, which depends on both the strong convexity moduli of the two functions and the upper and lower bounds on the spectrum of the variable metric operators. We validate the proposed algorithm, named Scaled Adaptive GEneralized FISTA (SAGE-FISTA), on exemplar image denoising and deblurring problems where edge-preserving Total Variation (TV) regularization is combined with Kullback-Leibler-type fidelity terms, as it is common in applications where signal-dependent Poisson noise is assumed in the data
Digital cultural heritage imaging via osmosis filtering
No full textIn Cultural Heritage (CH) imaging, data acquired within different spectral regions are often used to inspect surface and sub-surface features. Due to the experimental setup, these images may suffer from intensity inhomogeneities, which may prevent conservators from distinguishing the physical properties of the object under restoration. Furthermore, in multi-modal imaging, the transfer of information between one modality to another is often used to integrate image contents. In this paper, we apply the image osmosis model proposed in [4, 10, 12] to solve correct these problems arising when diagnostic CH imaging techniques based on reflectance, emission and fluorescence mode in the optical and thermal range are used. For an efficient computation, we use stable operator splitting techniques to solve the discretised model. We test our methods on real artwork datasets: the thermal measurements of the mural painting “Monocromo” by Leonardo Da Vinci, the UV-VIS-IR imaging of an ancient Russian icon and the Archimedes Palimpsest dataset
Special issue on optimization and learning methods for inverse problems in microscopy: in memory of Mario Bertero
No full textEditorial in memory of Mario Berter
Alternating Direction Implicit (ADI) schemes for a PDE-based image osmosis model
No full textWe consider Alternating Direction Implicit (ADI) splitting schemes to compute efficiently the numerical solution of the PDE osmosis model considered by Weickert et al. in [10] for several imaging applications. The discretised scheme is shown to preserve analogous properties to the continuous model. The dimensional splitting strategy traduces numerically into the solution of simple tridiagonal systems for which standard matrix factorisation techniques can be used to improve upon the performance of classical implicit methods, even for large time steps. Applications to the shadow removal problem are presented
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