21,502 research outputs found
Image Restoration Using Joint Statistical Modeling in Space-Transform Domain
This paper presents a novel strategy for high-fidelity image restoration by
characterizing both local smoothness and nonlocal self-similarity of natural
images in a unified statistical manner. The main contributions are three-folds.
First, from the perspective of image statistics, a joint statistical modeling
(JSM) in an adaptive hybrid space-transform domain is established, which offers
a powerful mechanism of combining local smoothness and nonlocal self-similarity
simultaneously to ensure a more reliable and robust estimation. Second, a new
form of minimization functional for solving image inverse problem is formulated
using JSM under regularization-based framework. Finally, in order to make JSM
tractable and robust, a new Split-Bregman based algorithm is developed to
efficiently solve the above severely underdetermined inverse problem associated
with theoretical proof of convergence. Extensive experiments on image
inpainting, image deblurring and mixed Gaussian plus salt-and-pepper noise
removal applications verify the effectiveness of the proposed algorithm.Comment: 14 pages, 18 figures, 7 Tables, to be published in IEEE Transactions
on Circuits System and Video Technology (TCSVT). High resolution pdf version
and Code can be found at: http://idm.pku.edu.cn/staff/zhangjian/IRJSM
A Bayesian fusion model for space-time reconstruction of finely resolved velocities in turbulent flows from low resolution measurements
The study of turbulent flows calls for measurements with high resolution both
in space and in time. We propose a new approach to reconstruct
High-Temporal-High-Spatial resolution velocity fields by combining two sources
of information that are well-resolved either in space or in time, the
Low-Temporal-High-Spatial (LTHS) and the High-Temporal-Low-Spatial (HTLS)
resolution measurements. In the framework of co-conception between sensing and
data post-processing, this work extensively investigates a Bayesian
reconstruction approach using a simulated database. A Bayesian fusion model is
developed to solve the inverse problem of data reconstruction. The model uses a
Maximum A Posteriori estimate, which yields the most probable field knowing the
measurements. The DNS of a wall-bounded turbulent flow at moderate Reynolds
number is used to validate and assess the performances of the present approach.
Low resolution measurements are subsampled in time and space from the fully
resolved data. Reconstructed velocities are compared to the reference DNS to
estimate the reconstruction errors. The model is compared to other conventional
methods such as Linear Stochastic Estimation and cubic spline interpolation.
Results show the superior accuracy of the proposed method in all
configurations. Further investigations of model performances on various range
of scales demonstrate its robustness. Numerical experiments also permit to
estimate the expected maximum information level corresponding to limitations of
experimental instruments.Comment: 15 pages, 6 figure
Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
We describe the first method to automatically estimate the 3D pose of the
human body as well as its 3D shape from a single unconstrained image. We
estimate a full 3D mesh and show that 2D joints alone carry a surprising amount
of information about body shape. The problem is challenging because of the
complexity of the human body, articulation, occlusion, clothing, lighting, and
the inherent ambiguity in inferring 3D from 2D. To solve this, we first use a
recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D
body joint locations. We then fit (top-down) a recently published statistical
body shape model, called SMPL, to the 2D joints. We do so by minimizing an
objective function that penalizes the error between the projected 3D model
joints and detected 2D joints. Because SMPL captures correlations in human
shape across the population, we are able to robustly fit it to very little
data. We further leverage the 3D model to prevent solutions that cause
interpenetration. We evaluate our method, SMPLify, on the Leeds Sports,
HumanEva, and Human3.6M datasets, showing superior pose accuracy with respect
to the state of the art.Comment: To appear in ECCV 201
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