10,517 research outputs found
Joint Maximum Purity Forest with Application to Image Super-Resolution
In this paper, we propose a novel random-forest scheme, namely Joint Maximum
Purity Forest (JMPF), for classification, clustering, and regression tasks. In
the JMPF scheme, the original feature space is transformed into a compactly
pre-clustered feature space, via a trained rotation matrix. The rotation matrix
is obtained through an iterative quantization process, where the input data
belonging to different classes are clustered to the respective vertices of the
new feature space with maximum purity. In the new feature space, orthogonal
hyperplanes, which are employed at the split-nodes of decision trees in random
forests, can tackle the clustering problems effectively. We evaluated our
proposed method on public benchmark datasets for regression and classification
tasks, and experiments showed that JMPF remarkably outperforms other
state-of-the-art random-forest-based approaches. Furthermore, we applied JMPF
to image super-resolution, because the transformed, compact features are more
discriminative to the clustering-regression scheme. Experiment results on
several public benchmark datasets also showed that the JMPF-based image
super-resolution scheme is consistently superior to recent state-of-the-art
image super-resolution algorithms.Comment: 18 pages, 7 figure
Image Super-Resolution Using Deep Convolutional Networks
We propose a deep learning method for single image super-resolution (SR). Our
method directly learns an end-to-end mapping between the low/high-resolution
images. The mapping is represented as a deep convolutional neural network (CNN)
that takes the low-resolution image as the input and outputs the
high-resolution one. We further show that traditional sparse-coding-based SR
methods can also be viewed as a deep convolutional network. But unlike
traditional methods that handle each component separately, our method jointly
optimizes all layers. Our deep CNN has a lightweight structure, yet
demonstrates state-of-the-art restoration quality, and achieves fast speed for
practical on-line usage. We explore different network structures and parameter
settings to achieve trade-offs between performance and speed. Moreover, we
extend our network to cope with three color channels simultaneously, and show
better overall reconstruction quality.Comment: 14 pages, 14 figures, journa
Ensemble Super-Resolution with A Reference Dataset
By developing sophisticated image priors or designing deep(er) architectures,
a variety of image Super-Resolution (SR) approaches have been proposed recently
and achieved very promising performance. A natural question that arises is
whether these methods can be reformulated into a unifying framework and whether
this framework assists in SR reconstruction? In this paper, we present a simple
but effective single image SR method based on ensemble learning, which can
produce a better performance than that could be obtained from any of SR methods
to be ensembled (or called component super-resolvers). Based on the assumption
that better component super-resolver should have larger ensemble weight when
performing SR reconstruction, we present a Maximum A Posteriori (MAP)
estimation framework for the inference of optimal ensemble weights. Specially,
we introduce a reference dataset, which is composed of High-Resolution (HR) and
Low-Resolution (LR) image pairs, to measure the super-resolution abilities
(prior knowledge) of different component super-resolvers. To obtain the optimal
ensemble weights, we propose to incorporate the reconstruction constraint,
which states that the degenerated HR image should be equal to the LR
observation one, as well as the prior knowledge of ensemble weights into the
MAP estimation framework. Moreover, the proposed optimization problem can be
solved by an analytical solution. We study the performance of the proposed
method by comparing with different competitive approaches, including four
state-of-the-art non-deep learning based methods, four latest deep learning
based methods and one ensemble learning based method, and prove its
effectiveness and superiority on three public datasets.Comment: 14 pages, 11 figure
A survey of sparse representation: algorithms and applications
Sparse representation has attracted much attention from researchers in fields
of signal processing, image processing, computer vision and pattern
recognition. Sparse representation also has a good reputation in both
theoretical research and practical applications. Many different algorithms have
been proposed for sparse representation. The main purpose of this article is to
provide a comprehensive study and an updated review on sparse representation
and to supply a guidance for researchers. The taxonomy of sparse representation
methods can be studied from various viewpoints. For example, in terms of
different norm minimizations used in sparsity constraints, the methods can be
roughly categorized into five groups: sparse representation with -norm
minimization, sparse representation with -norm (0p1) minimization,
sparse representation with -norm minimization and sparse representation
with -norm minimization. In this paper, a comprehensive overview of
sparse representation is provided. The available sparse representation
algorithms can also be empirically categorized into four groups: greedy
strategy approximation, constrained optimization, proximity algorithm-based
optimization, and homotopy algorithm-based sparse representation. The
rationales of different algorithms in each category are analyzed and a wide
range of sparse representation applications are summarized, which could
sufficiently reveal the potential nature of the sparse representation theory.
Specifically, an experimentally comparative study of these sparse
representation algorithms was presented. The Matlab code used in this paper can
be available at: http://www.yongxu.org/lunwen.html.Comment: Published on IEEE Access, Vol. 3, pp. 490-530, 201
Single image super-resolution by approximated Heaviside functions
Image super-resolution is a process to enhance image resolution. It is widely
used in medical imaging, satellite imaging, target recognition, etc. In this
paper, we conduct continuous modeling and assume that the unknown image
intensity function is defined on a continuous domain and belongs to a space
with a redundant basis. We propose a new iterative model for single image
super-resolution based on an observation: an image is consisted of smooth
components and non-smooth components, and we use two classes of approximated
Heaviside functions (AHFs) to represent them respectively. Due to sparsity of
the non-smooth components, a model is employed. In addition, we apply
the proposed iterative model to image patches to reduce computation and
storage. Comparisons with some existing competitive methods show the
effectiveness of the proposed method
Face Hallucination using Linear Models of Coupled Sparse Support
Most face super-resolution methods assume that low-resolution and
high-resolution manifolds have similar local geometrical structure, hence learn
local models on the lowresolution manifolds (e.g. sparse or locally linear
embedding models), which are then applied on the high-resolution manifold.
However, the low-resolution manifold is distorted by the oneto-many
relationship between low- and high- resolution patches. This paper presents a
method which learns linear models based on the local geometrical structure on
the high-resolution manifold rather than on the low-resolution manifold. For
this, in a first step, the low-resolution patch is used to derive a globally
optimal estimate of the high-resolution patch. The approximated solution is
shown to be close in Euclidean space to the ground-truth but is generally
smooth and lacks the texture details needed by state-ofthe-art face
recognizers. This first estimate allows us to find the support of the
high-resolution manifold using sparse coding (SC), which are then used as
support for learning a local projection (or upscaling) model between the
low-resolution and the highresolution manifolds using Multivariate Ridge
Regression (MRR). Experimental results show that the proposed method
outperforms six face super-resolution methods in terms of both recognition and
quality. These results also reveal that the recognition and quality are
significantly affected by the method used for stitching all super-resolved
patches together, where quilting was found to better preserve the texture
details which helps to achieve higher recognition rates
Image Super-Resolution via Sparse Bayesian Modeling of Natural Images
Image super-resolution (SR) is one of the long-standing and active topics in
image processing community. A large body of works for image super resolution
formulate the problem with Bayesian modeling techniques and then obtain its
Maximum-A-Posteriori (MAP) solution, which actually boils down to a regularized
regression task over separable regularization term. Although straightforward,
this approach cannot exploit the full potential offered by the probabilistic
modeling, as only the posterior mode is sought. Also, the separable property of
the regularization term can not capture any correlations between the sparse
coefficients, which sacrifices much on its modeling accuracy. We propose a
Bayesian image SR algorithm via sparse modeling of natural images. The sparsity
property of the latent high resolution image is exploited by introducing latent
variables into the high-order Markov Random Field (MRF) which capture the
content adaptive variance by pixel-wise adaptation. The high-resolution image
is estimated via Empirical Bayesian estimation scheme, which is substantially
faster than our previous approach based on Markov Chain Monte Carlo sampling
[1]. It is shown that the actual cost function for the proposed approach
actually incorporates a non-factorial regularization term over the sparse
coefficients. Experimental results indicate that the proposed method can
generate competitive or better results than \emph{state-of-the-art} SR
algorithms.Comment: 8 figures, 29 page
EDIZ: An Error Diffusion Image Zooming Scheme
Interpolation based image zooming methods provide a high execution speed and
low computational complexity. However, the quality of the zoomed images is
unsatisfactory in many cases. The main challenge of super- resolution methods
is to create new details to the image. This paper proposes a new algorithm to
create new details using a zoom-out-zoom-in strategy. This strategy permits
reducing blurring effects by adding the estimated error to the final image.
Experimental results for natural images confirm the algorithm's ability to
create visually pleasing results.Comment: Submitted to IEEE Signal Processing Letter
Face Recognition in Low Quality Images: A Survey
Low-resolution face recognition (LRFR) has received increasing attention over
the past few years. Its applications lie widely in the real-world environment
when high-resolution or high-quality images are hard to capture. One of the
biggest demands for LRFR technologies is video surveillance. As the the number
of surveillance cameras in the city increases, the videos that captured will
need to be processed automatically. However, those videos or images are usually
captured with large standoffs, arbitrary illumination condition, and diverse
angles of view. Faces in these images are generally small in size. Several
studies addressed this problem employed techniques like super resolution,
deblurring, or learning a relationship between different resolution domains. In
this paper, we provide a comprehensive review of approaches to low-resolution
face recognition in the past five years. First, a general problem definition is
given. Later, systematically analysis of the works on this topic is presented
by catogory. In addition to describing the methods, we also focus on datasets
and experiment settings. We further address the related works on unconstrained
low-resolution face recognition and compare them with the result that use
synthetic low-resolution data. Finally, we summarized the general limitations
and speculate a priorities for the future effort.Comment: There are some mistakes addressing in this paper which will be
misleading to the reader and we wont have a new version in short time. We
will resubmit once it is being corecte
Selective Image Super-Resolution
In this paper we propose a vision system that performs image Super Resolution
(SR) with selectivity. Conventional SR techniques, either by multi-image fusion
or example-based construction, have failed to capitalize on the intrinsic
structural and semantic context in the image, and performed "blind" resolution
recovery to the entire image area. By comparison, we advocate example-based
selective SR whereby selectivity is exemplified in three aspects: region
selectivity (SR only at object regions), source selectivity (object SR with
trained object dictionaries), and refinement selectivity (object boundaries
refinement using matting). The proposed system takes over-segmented
low-resolution images as inputs, assimilates recent learning techniques of
sparse coding (SC) and grouped multi-task lasso (GMTL), and leads eventually to
a framework for joint figure-ground separation and interest object SR. The
efficiency of our framework is manifested in our experiments with subsets of
the VOC2009 and MSRC datasets. We also demonstrate several interesting vision
applications that can build on our system.Comment: 20 pages, 5 figures. Submitted to Computer Vision and Image
Understanding in March 2010. Keywords: image super resolution, semantic image
segmentation, vision system, vision applicatio
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