15,945 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
Universal Denoising Networks : A Novel CNN Architecture for Image Denoising
We design a novel network architecture for learning discriminative image
models that are employed to efficiently tackle the problem of grayscale and
color image denoising. Based on the proposed architecture, we introduce two
different variants. The first network involves convolutional layers as a core
component, while the second one relies instead on non-local filtering layers
and thus it is able to exploit the inherent non-local self-similarity property
of natural images. As opposed to most of the existing deep network approaches,
which require the training of a specific model for each considered noise level,
the proposed models are able to handle a wide range of noise levels using a
single set of learned parameters, while they are very robust when the noise
degrading the latent image does not match the statistics of the noise used
during training. The latter argument is supported by results that we report on
publicly available images corrupted by unknown noise and which we compare
against solutions obtained by competing methods. At the same time the
introduced networks achieve excellent results under additive white Gaussian
noise (AWGN), which are comparable to those of the current state-of-the-art
network, while they depend on a more shallow architecture with the number of
trained parameters being one order of magnitude smaller. These properties make
the proposed networks ideal candidates to serve as sub-solvers on restoration
methods that deal with general inverse imaging problems such as deblurring,
demosaicking, superresolution, etc.Comment: Camera ready paper to appear in the Proceedings of CVPR 201
Discriminative Transfer Learning for General Image Restoration
Recently, several discriminative learning approaches have been proposed for
effective image restoration, achieving convincing trade-off between image
quality and computational efficiency. However, these methods require separate
training for each restoration task (e.g., denoising, deblurring, demosaicing)
and problem condition (e.g., noise level of input images). This makes it
time-consuming and difficult to encompass all tasks and conditions during
training. In this paper, we propose a discriminative transfer learning method
that incorporates formal proximal optimization and discriminative learning for
general image restoration. The method requires a single-pass training and
allows for reuse across various problems and conditions while achieving an
efficiency comparable to previous discriminative approaches. Furthermore, after
being trained, our model can be easily transferred to new likelihood terms to
solve untrained tasks, or be combined with existing priors to further improve
image restoration quality
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