12,167 research outputs found
Steered mixture-of-experts for light field images and video : representation and coding
Research in light field (LF) processing has heavily increased over the last decade. This is largely driven by the desire to achieve the same level of immersion and navigational freedom for camera-captured scenes as it is currently available for CGI content. Standardization organizations such as MPEG and JPEG continue to follow conventional coding paradigms in which viewpoints are discretely represented on 2-D regular grids. These grids are then further decorrelated through hybrid DPCM/transform techniques. However, these 2-D regular grids are less suited for high-dimensional data, such as LFs. We propose a novel coding framework for higher-dimensional image modalities, called Steered Mixture-of-Experts (SMoE). Coherent areas in the higher-dimensional space are represented by single higher-dimensional entities, called kernels. These kernels hold spatially localized information about light rays at any angle arriving at a certain region. The global model consists thus of a set of kernels which define a continuous approximation of the underlying plenoptic function. We introduce the theory of SMoE and illustrate its application for 2-D images, 4-D LF images, and 5-D LF video. We also propose an efficient coding strategy to convert the model parameters into a bitstream. Even without provisions for high-frequency information, the proposed method performs comparable to the state of the art for low-to-mid range bitrates with respect to subjective visual quality of 4-D LF images. In case of 5-D LF video, we observe superior decorrelation and coding performance with coding gains of a factor of 4x in bitrate for the same quality. At least equally important is the fact that our method inherently has desired functionality for LF rendering which is lacking in other state-of-the-art techniques: (1) full zero-delay random access, (2) light-weight pixel-parallel view reconstruction, and (3) intrinsic view interpolation and super-resolution
A symbol-based algorithm for decoding bar codes
We investigate the problem of decoding a bar code from a signal measured with
a hand-held laser-based scanner. Rather than formulating the inverse problem as
one of binary image reconstruction, we instead incorporate the symbology of the
bar code into the reconstruction algorithm directly, and search for a sparse
representation of the UPC bar code with respect to this known dictionary. Our
approach significantly reduces the degrees of freedom in the problem, allowing
for accurate reconstruction that is robust to noise and unknown parameters in
the scanning device. We propose a greedy reconstruction algorithm and provide
robust reconstruction guarantees. Numerical examples illustrate the
insensitivity of our symbology-based reconstruction to both imprecise model
parameters and noise on the scanned measurements.Comment: 24 pages, 12 figure
Deep Learning networks with p-norm loss layers for spatial resolution enhancement of 3D medical images
Thurnhofer-Hemsi K., López-Rubio E., Roé-Vellvé N., Molina-Cabello M.A. (2019) Deep Learning Networks with p-norm Loss Layers for Spatial Resolution Enhancement of 3D Medical Images. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science, vol 11487. Springer, ChamNowadays, obtaining high-quality magnetic resonance (MR) images is a complex problem due to several acquisition factors, but is crucial in order to perform good diagnostics. The enhancement of the resolution is a typical procedure applied after the image generation. State-of-the-art works gather a large variety of methods for super-resolution (SR), among which deep learning has become very popular during the last years. Most of the SR deep-learning methods are based on the min-
imization of the residuals by the use of Euclidean loss layers. In this paper, we propose an SR model based on the use of a p-norm loss layer to improve the learning process and obtain a better high-resolution (HR) image. This method was implemented using a three-dimensional convolutional neural network (CNN), and tested for several norms in order to determine the most robust t. The proposed methodology was trained and tested with sets of MR structural T1-weighted images and showed
better outcomes quantitatively, in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the restored and the calculated residual images showed better CNN outputs.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Adaptive Image Denoising by Targeted Databases
We propose a data-dependent denoising procedure to restore noisy images.
Different from existing denoising algorithms which search for patches from
either the noisy image or a generic database, the new algorithm finds patches
from a database that contains only relevant patches. We formulate the denoising
problem as an optimal filter design problem and make two contributions. First,
we determine the basis function of the denoising filter by solving a group
sparsity minimization problem. The optimization formulation generalizes
existing denoising algorithms and offers systematic analysis of the
performance. Improvement methods are proposed to enhance the patch search
process. Second, we determine the spectral coefficients of the denoising filter
by considering a localized Bayesian prior. The localized prior leverages the
similarity of the targeted database, alleviates the intensive Bayesian
computation, and links the new method to the classical linear minimum mean
squared error estimation. We demonstrate applications of the proposed method in
a variety of scenarios, including text images, multiview images and face
images. Experimental results show the superiority of the new algorithm over
existing methods.Comment: 15 pages, 13 figures, 2 tables, journa
LOFAR Sparse Image Reconstruction
Context. The LOw Frequency ARray (LOFAR) radio telescope is a giant digital
phased array interferometer with multiple antennas distributed in Europe. It
provides discrete sets of Fourier components of the sky brightness. Recovering
the original brightness distribution with aperture synthesis forms an inverse
problem that can be solved by various deconvolution and minimization methods
Aims. Recent papers have established a clear link between the discrete nature
of radio interferometry measurement and the "compressed sensing" (CS) theory,
which supports sparse reconstruction methods to form an image from the measured
visibilities. Empowered by proximal theory, CS offers a sound framework for
efficient global minimization and sparse data representation using fast
algorithms. Combined with instrumental direction-dependent effects (DDE) in the
scope of a real instrument, we developed and validated a new method based on
this framework Methods. We implemented a sparse reconstruction method in the
standard LOFAR imaging tool and compared the photometric and resolution
performance of this new imager with that of CLEAN-based methods (CLEAN and
MS-CLEAN) with simulated and real LOFAR data Results. We show that i) sparse
reconstruction performs as well as CLEAN in recovering the flux of point
sources; ii) performs much better on extended objects (the root mean square
error is reduced by a factor of up to 10); and iii) provides a solution with an
effective angular resolution 2-3 times better than the CLEAN images.
Conclusions. Sparse recovery gives a correct photometry on high dynamic and
wide-field images and improved realistic structures of extended sources (of
simulated and real LOFAR datasets). This sparse reconstruction method is
compatible with modern interferometric imagers that handle DDE corrections (A-
and W-projections) required for current and future instruments such as LOFAR
and SKAComment: Published in A&A, 19 pages, 9 figure
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
Plug-and-play (PnP) is a non-convex framework that integrates modern
denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or
other proximal algorithms. An advantage of PnP is that one can use pre-trained
denoisers when there is not sufficient data for end-to-end training. Although
PnP has been recently studied extensively with great empirical success,
theoretical analysis addressing even the most basic question of convergence has
been insufficient. In this paper, we theoretically establish convergence of
PnP-FBS and PnP-ADMM, without using diminishing stepsizes, under a certain
Lipschitz condition on the denoisers. We then propose real spectral
normalization, a technique for training deep learning-based denoisers to
satisfy the proposed Lipschitz condition. Finally, we present experimental
results validating the theory.Comment: Published in the International Conference on Machine Learning, 201
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