22 research outputs found
Single image example-based super-resolution using cross-scale patch matching and Markov random field modelling
Example-based super-resolution has become increasingly popular over the last few years for its ability to overcome the limitations of classical multi-frame approach. In this paper we present a new example-based method that uses the input low-resolution image itself as a search space for high-resolution patches by exploiting self-similarity across different resolution scales. Found examples are combined in a high-resolution image by the means of Markov Random Field modelling that forces their global agreement. Additionally, we apply back-projection and steering kernel regression as post-processing techniques. In this way, we are able to produce sharp and artefact-free results that are comparable or better than standard interpolation and state-of-the-art super-resolution techniques
Region graph partition function expansion and approximate free energy landscapes: Theory and some numerical results
Graphical models for finite-dimensional spin glasses and real-world
combinatorial optimization and satisfaction problems usually have an abundant
number of short loops. The cluster variation method and its extension, the
region graph method, are theoretical approaches for treating the complicated
short-loop-induced local correlations. For graphical models represented by
non-redundant or redundant region graphs, approximate free energy landscapes
are constructed in this paper through the mathematical framework of region
graph partition function expansion. Several free energy functionals are
obtained, each of which use a set of probability distribution functions or
functionals as order parameters. These probability distribution
function/functionals are required to satisfy the region graph
belief-propagation equation or the region graph survey-propagation equation to
ensure vanishing correction contributions of region subgraphs with dangling
edges. As a simple application of the general theory, we perform region graph
belief-propagation simulations on the square-lattice ferromagnetic Ising model
and the Edwards-Anderson model. Considerable improvements over the conventional
Bethe-Peierls approximation are achieved. Collective domains of different sizes
in the disordered and frustrated square lattice are identified by the
message-passing procedure. Such collective domains and the frustrations among
them are responsible for the low-temperature glass-like dynamical behaviors of
the system.Comment: 30 pages, 11 figures. More discussion on redundant region graphs. To
be published by Journal of Statistical Physic
Replica Shuffled Iterative Decoding
Replica shuffled versions of iterative decoders of turbo codes, low-density parity-check codes and turbo product codes are presented. The proposed schemes converge faster than standard and previously proposed "shuffled" approaches. Simulations show that the new schedules offer good performance versus complexity/latency trade-offs
Efficient Peer-to-Peer Belief Propagation ∗
In this paper, we will present an efficient approach for distributed inference. We use belief propagation’s message-passing algorithm on top of a DHT storing a Bayesian network. Nodes in the DHT run a variant of the spring relaxation algorithm to redistribute the Bayesian network among them. Thereafter correlated data is stored close to each other reducing the message cost for inference. We simulated our approach in Matlab and show the message reduction and the achieved load balance for random, tree-shaped, and scale-free Bayesian networks of different sizes. As possible application, we envision a distributed software knowledge base maintaining encountered software bugs under users ’ system configurations together with possible solutions for other users having similar problems. Users would not only be able to repair their system but also to foresee possible problems if they would install software updates or new applications.