266 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
Collaborative Representation based Classification for Face Recognition
By coding a query sample as a sparse linear combination of all training
samples and then classifying it by evaluating which class leads to the minimal
coding residual, sparse representation based classification (SRC) leads to
interesting results for robust face recognition. It is widely believed that the
l1- norm sparsity constraint on coding coefficients plays a key role in the
success of SRC, while its use of all training samples to collaboratively
represent the query sample is rather ignored. In this paper we discuss how SRC
works, and show that the collaborative representation mechanism used in SRC is
much more crucial to its success of face classification. The SRC is a special
case of collaborative representation based classification (CRC), which has
various instantiations by applying different norms to the coding residual and
coding coefficient. More specifically, the l1 or l2 norm characterization of
coding residual is related to the robustness of CRC to outlier facial pixels,
while the l1 or l2 norm characterization of coding coefficient is related to
the degree of discrimination of facial features. Extensive experiments were
conducted to verify the face recognition accuracy and efficiency of CRC with
different instantiations.Comment: It is a substantial revision of a previous conference paper (L.
Zhang, M. Yang, et al. "Sparse Representation or Collaborative
Representation: Which Helps Face Recognition?" in ICCV 2011
Video Face Super-Resolution with Motion-Adaptive Feedback Cell
Video super-resolution (VSR) methods have recently achieved a remarkable
success due to the development of deep convolutional neural networks (CNN).
Current state-of-the-art CNN methods usually treat the VSR problem as a large
number of separate multi-frame super-resolution tasks, at which a batch of low
resolution (LR) frames is utilized to generate a single high resolution (HR)
frame, and running a slide window to select LR frames over the entire video
would obtain a series of HR frames. However, duo to the complex temporal
dependency between frames, with the number of LR input frames increase, the
performance of the reconstructed HR frames become worse. The reason is in that
these methods lack the ability to model complex temporal dependencies and hard
to give an accurate motion estimation and compensation for VSR process. Which
makes the performance degrade drastically when the motion in frames is complex.
In this paper, we propose a Motion-Adaptive Feedback Cell (MAFC), a simple but
effective block, which can efficiently capture the motion compensation and feed
it back to the network in an adaptive way. Our approach efficiently utilizes
the information of the inter-frame motion, the dependence of the network on
motion estimation and compensation method can be avoid. In addition, benefiting
from the excellent nature of MAFC, the network can achieve better performance
in the case of extremely complex motion scenarios. Extensive evaluations and
comparisons validate the strengths of our approach, and the experimental
results demonstrated that the proposed framework is outperform the
state-of-the-art methods.Comment: To appear in AAAI 202
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