1,088 research outputs found
Adaptive Digital Scan Variable Pixels
The square and rectangular shape of the pixels in the digital images for
sensing and display purposes introduces several inaccuracies in the
representation of digital images. The major disadvantage of square pixel shapes
is the inability to accurately capture and display the details in the objects
having variable orientations to edges, shapes and regions. This effect can be
observed by the inaccurate representation of diagonal edges in low resolution
square pixel images. This paper explores a less investigated idea of using
variable shaped pixels for improving visual quality of image scans without
increasing the square pixel resolution. The proposed adaptive filtering
technique reports an improvement in image PSNR.Comment: 4th International Conference on Advances in Computing, Communications
and Informatics, August, 201
Computation and visualization of photonic quasicrystal spectra via Blochs theorem
Previous methods for determining photonic quasicrystal (PQC) spectra have
relied on the use of large supercells to compute the eigenfrequencies and/or
local density of states (LDOS). In this manuscript, we present a method by
which the energy spectrum and the eigenstates of a PQC can be obtained by
solving Maxwells equations in higher dimensions for any PQC defined by the
standard cut-and-project construction, to which a generalization of Blochs
theorem applies. In addition, we demonstrate how one can compute band
structures with defect states in the higher-dimensional superspace with no
additional computational cost. As a proof of concept, these general ideas are
demonstrated for the simple case of one-dimensional quasicrystals, which can
also be solved by simple transfer-matrix techniques.Comment: Published in Physical Review B, 77 104201, 200
Sub-pixel Layout for Super-Resolution with Images in the Octic Group
13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part IThis paper presents a novel super-resolution framework by exploring the properties of non-conventional pixel layouts and shapes. We show that recording multiple images, transformed in the octic group, with a sensor of asymmetric sub-pixel layout increases the spatial sampling compared to a conventional sensor with a rectilinear grid of pixels and hence increases the image resolution. We further prove a theoretical bound for achieving well-posed super-resolution with a designated magnification factor w.r.t. the number and distribution of sub-pixels. We also propose strategies for selecting good sub-pixel layouts and effective super-resolution algorithms for our setup. The experimental results validate the proposed theory and solution, which have the potential to guide the future CCD layout design with super-resolution functionality.United States. Air Force (Assistant Secretary of Defense for Research & Engineering Contract #FA8721-05-C-0002)SUTD-MIT International Design Centre (Joint Postdoctoral Programme)Singapore University of Technology and Design (SUTD StartUp Grant ISTD 2011 016)Singapore. Ministry of Education (MOE Academic Research Fund MOE2013-T2-1-159
Learning Moore-Penrose based residuals for robust non-blind image deconvolution
This work was supported by grants P20_00286 and B-TIC-324-UGR20 funded by Consejería de Universidad, Investigación e Innovación ( Junta de Andalucía ) and by “ ERDF A way of making Europe”. Funding for open access charge: Universidad de Granada / CBUA.This paper proposes a deep learning-based method for image restoration given an inaccurate knowledge of the degradation. We first show how the impulse response of a Wiener filter can approximate the Moore-Penrose pseudo-inverse of the blur convolution operator. The deconvolution problem is then cast as the learning of a residual in the null space of the blur kernel, which, when added to the Wiener restoration, will satisfy the image formation model. This approach is expected to make the network capable of dealing with different blurs since only residuals associated with the Wiener filter have to be learned. Artifacts caused by inaccuracies in the blur estimation and other image formation model inconsistencies are removed by a Dynamic Filter Network. The extensive experiments carried out on several synthetic and real image datasets assert the proposed method's performance and robustness and demonstrate the advantage of the proposed method over existing ones.Junta de Andalucía P20_00286, B-TIC-324-UGR20ERDF A way of making EuropeUniversidad de Granada / CBU
High Quality Image Interpolation via Local Autoregressive and Nonlocal 3-D Sparse Regularization
In this paper, we propose a novel image interpolation algorithm, which is
formulated via combining both the local autoregressive (AR) model and the
nonlocal adaptive 3-D sparse model as regularized constraints under the
regularization framework. Estimating the high-resolution image by the local AR
regularization is different from these conventional AR models, which weighted
calculates the interpolation coefficients without considering the rough
structural similarity between the low-resolution (LR) and high-resolution (HR)
images. Then the nonlocal adaptive 3-D sparse model is formulated to regularize
the interpolated HR image, which provides a way to modify these pixels with the
problem of numerical stability caused by AR model. In addition, a new
Split-Bregman based iterative algorithm is developed to solve the above
optimization problem iteratively. Experiment results demonstrate that the
proposed algorithm achieves significant performance improvements over the
traditional algorithms in terms of both objective quality and visual perceptionComment: 4 pages, 5 figures, 2 tables, to be published at IEEE Visual
Communications and Image Processing (VCIP) 201
Conformal Cyclic Cosmology Signatures and Anomalies of the CMB Sky
Circles of low-variance and Hawking points in the Cosmic Microwave Background
(CMB), resulting from black hole mergers and black hole evaporation,
respectively, in a previous cycle of the universe, have been predicted as
possible evidence for the Conformal Cyclic Cosmology model (CCC) introduced by
R. Penrose. We present a high-resolution search for such low-variance circles
in the Planck and WMAP CMB data, and introduce HawkingNet, our machine learning
open-source software based on a ResNet18 algorithm, to search for Hawking
points in the CMB. We find that CMB anomalies, consisting of a few bright
pixels, erroneously lead to regions with many low-variance circles, and
consequently sets of concentric low-variance circles, when applying the search
criteria used in previous work [V.G. Gurzadyan, R. Penrose]. After removing the
anomalies from the data no statistically significant low-variance circles can
be found. Concerning Hawking points, also no statistically significant evidence
is found when using a Gaussian temperature amplitude model over 1 degree
opening angle and after accounting for CMB anomalies. That CMB anomalies
themselves might be remnants of Hawking points is not supported by low-variance
and/or low-temperature circles around them. The absence of such
statistically-significant distinct features in the currently available CMB data
does not disprove the CCC model but implies that higher resolution CMB data
and/or refined CCC based predictions are needed to pursue the search for CCC
signatures.Comment: prepared for JCAP rev
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Face image super-resolution using 2D CCA
In this paper a face super-resolution method using two-dimensional canonical correlation analysis (2D CCA) is presented. A detail compensation step is followed to add high-frequency components to the reconstructed high-resolution face. Unlike most of the previous researches on face super-resolution algorithms that first transform the images into vectors, in our approach the relationship between the high-resolution and the low-resolution face image are maintained in their original 2D representation. In addition, rather than approximating the entire face, different parts of a face image are super-resolved separately to better preserve the local structure. The proposed method is compared with various state-of-the-art super-resolution algorithms using multiple evaluation criteria including face recognition performance. Results on publicly available datasets show that the proposed method super-resolves high quality face images which are very close to the ground-truth and performance gain is not dataset dependent. The method is very efficient in both the training and testing phases compared to the other approaches. © 2013 Elsevier B.V
Signal reconstruction via operator guiding
Signal reconstruction from a sample using an orthogonal projector onto a
guiding subspace is theoretically well justified, but may be difficult to
practically implement. We propose more general guiding operators, which
increase signal components in the guiding subspace relative to those in a
complementary subspace, e.g., iterative low-pass edge-preserving filters for
super-resolution of images. Two examples of super-resolution illustrate our
technology: a no-flash RGB photo guided using a high resolution flash RGB
photo, and a depth image guided using a high resolution RGB photo.Comment: 5 pages, 8 figures. To appear in Proceedings of SampTA 2017: Sampling
Theory and Applications, 12th International Conference, July 3-7, 2017,
Tallinn, Estoni
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