27,846 research outputs found
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
Combinatorial persistency criteria for multicut and max-cut
In combinatorial optimization, partial variable assignments are called
persistent if they agree with some optimal solution. We propose persistency
criteria for the multicut and max-cut problem as well as fast combinatorial
routines to verify them. The criteria that we derive are based on mappings that
improve feasible multicuts, respectively cuts. Our elementary criteria can be
checked enumeratively. The more advanced ones rely on fast algorithms for upper
and lower bounds for the respective cut problems and max-flow techniques for
auxiliary min-cut problems. Our methods can be used as a preprocessing
technique for reducing problem sizes or for computing partial optimality
guarantees for solutions output by heuristic solvers. We show the efficacy of
our methods on instances of both problems from computer vision, biomedical
image analysis and statistical physics
Mixed spatially varying -BV regularization of inverse ill-posed problems
Several generalizations of the traditional Tikhonov-Phillips regularization
method have been proposed during the last two decades. Many of these
generalizations are based upon inducing stability throughout the use of
different penalizers which allow the capturing of diverse properties of the
exact solution (e.g. edges, discontinuities, borders, etc.). However, in some
problems in which it is known that the regularity of the exact solution is
heterogeneous and/or anisotropic, it is reasonable to think that a much better
option could be the simultaneous use of two or more penalizers of different
nature. Such is the case, for instance, in some image restoration problems in
which preservation of edges, borders or discontinuities is an important matter.
In this work we present some results on the simultaneous use of penalizers of
and of bounded variation (BV) type. For particular cases, existence and
uniqueness results are proved. Open problems are discussed and results to
signal restoration problems are presented.Comment: 18 pages, 12 figure
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
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