5,076 research outputs found
A flexible space-variant anisotropic regularisation for image restoration with automated parameter selection
We propose a new space-variant anisotropic regularisation term for
variational image restoration, based on the statistical assumption that the
gradients of the target image distribute locally according to a bivariate
generalised Gaussian distribution. The highly flexible variational structure of
the corresponding regulariser encodes several free parameters which hold the
potential for faithfully modelling the local geometry in the image and
describing local orientation preferences. For an automatic estimation of such
parameters, we design a robust maximum likelihood approach and report results
on its reliability on synthetic data and natural images. For the numerical
solution of the corresponding image restoration model, we use an iterative
algorithm based on the Alternating Direction Method of Multipliers (ADMM). A
suitable preliminary variable splitting together with a novel result in
multivariate non-convex proximal calculus yield a very efficient minimisation
algorithm. Several numerical results showing significant quality-improvement of
the proposed model with respect to some related state-of-the-art competitors
are reported, in particular in terms of texture and detail preservation
A Brief Survey of Recent Edge-Preserving Smoothers
We introduce recent and very recent smoothing methods and discuss them in the common framework of `energy functions'. Focus is on the preservation of boundaries, spikes and canyons in presence of noise
Unsupervised bayesian convex deconvolution based on a field with an explicit partition function
This paper proposes a non-Gaussian Markov field with a special feature: an
explicit partition function. To the best of our knowledge, this is an original
contribution. Moreover, the explicit expression of the partition function
enables the development of an unsupervised edge-preserving convex deconvolution
method. The method is fully Bayesian, and produces an estimate in the sense of
the posterior mean, numerically calculated by means of a Monte-Carlo Markov
Chain technique. The approach is particularly effective and the computational
practicability of the method is shown on a simple simulated example
Detection of dirt impairments from archived film sequences : survey and evaluations
Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research
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