71 research outputs found
Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal
In this paper, we address the problem of estimating and removing non-uniform
motion blur from a single blurry image. We propose a deep learning approach to
predicting the probabilistic distribution of motion blur at the patch level
using a convolutional neural network (CNN). We further extend the candidate set
of motion kernels predicted by the CNN using carefully designed image
rotations. A Markov random field model is then used to infer a dense
non-uniform motion blur field enforcing motion smoothness. Finally, motion blur
is removed by a non-uniform deblurring model using patch-level image prior.
Experimental evaluations show that our approach can effectively estimate and
remove complex non-uniform motion blur that is not handled well by previous
approaches.Comment: This is a final version accepted by CVPR 201
Capturing Topology in Graph Pattern Matching
Graph pattern matching is often defined in terms of subgraph isomorphism, an
NP-complete problem. To lower its complexity, various extensions of graph
simulation have been considered instead. These extensions allow pattern
matching to be conducted in cubic-time. However, they fall short of capturing
the topology of data graphs, i.e., graphs may have a structure drastically
different from pattern graphs they match, and the matches found are often too
large to understand and analyze. To rectify these problems, this paper proposes
a notion of strong simulation, a revision of graph simulation, for graph
pattern matching. (1) We identify a set of criteria for preserving the topology
of graphs matched. We show that strong simulation preserves the topology of
data graphs and finds a bounded number of matches. (2) We show that strong
simulation retains the same complexity as earlier extensions of simulation, by
providing a cubic-time algorithm for computing strong simulation. (3) We
present the locality property of strong simulation, which allows us to
effectively conduct pattern matching on distributed graphs. (4) We
experimentally verify the effectiveness and efficiency of these algorithms,
using real-life data and synthetic data.Comment: VLDB201
Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements
Background subtraction has been a fundamental and widely studied task in
video analysis, with a wide range of applications in video surveillance,
teleconferencing and 3D modeling. Recently, motivated by compressive imaging,
background subtraction from compressive measurements (BSCM) is becoming an
active research task in video surveillance. In this paper, we propose a novel
tensor-based robust PCA (TenRPCA) approach for BSCM by decomposing video frames
into backgrounds with spatial-temporal correlations and foregrounds with
spatio-temporal continuity in a tensor framework. In this approach, we use 3D
total variation (TV) to enhance the spatio-temporal continuity of foregrounds,
and Tucker decomposition to model the spatio-temporal correlations of video
background. Based on this idea, we design a basic tensor RPCA model over the
video frames, dubbed as the holistic TenRPCA model (H-TenRPCA). To characterize
the correlations among the groups of similar 3D patches of video background, we
further design a patch-group-based tensor RPCA model (PG-TenRPCA) by joint
tensor Tucker decompositions of 3D patch groups for modeling the video
background. Efficient algorithms using alternating direction method of
multipliers (ADMM) are developed to solve the proposed models. Extensive
experiments on simulated and real-world videos demonstrate the superiority of
the proposed approaches over the existing state-of-the-art approaches.Comment: To appear in IEEE TI
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