Results 351 to 360 of about 302,460 (378)

The Fractional Laplacian

2016
The fractional Laplacian in one dimension Random walkers with constant steps Ordinary diffusion Random jumpers Central limit theorem and stable distributions Power-law probability jump lengths A principal-value integral Wires and springs The fractional Laplacian Fourier transform Effect of fractional order Numerical computation of the fractional ...
C. Pozrikidis
openaire   +3 more sources

Underwater Image Enhancement With Hyper-Laplacian Reflectance Priors

IEEE Transactions on Image Processing, 2022
Underwater image enhancement aims at improving the visibility and eliminating color distortions of underwater images degraded by light absorption and scattering in water.
Peixian Zhuang, Jiamin Wu, F. Porikli, Chongyi Li   +3 more
semanticscholar   +1 more source

Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals

IEEE transactions on circuits and systems for video technology (Print), 2021
With a great success of the generative model via deep neural networks, monocular depth estimation has been actively studied by exploiting various encoder-decoder architectures.
Minsoo Song, Seokjae Lim, Wonjun Kim
semanticscholar   +1 more source

DSLR: Deep Stacked Laplacian Restorer for Low-Light Image Enhancement

IEEE transactions on multimedia, 2021
Various images captured in complicated lighting conditions often suffer from deterioration of the image quality. Such poor quality not only dissatisfies the user expectation but also may lead to a significant performance drop in many applications.
Seokjae Lim, Wonjun Kim
semanticscholar   +1 more source

Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks

International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020
Deep learning-based methods have recently demonstrated promising results in deformable image registration for a wide range of medical image analysis tasks.
Tony C. W. Mok, Albert C. S. Chung
semanticscholar   +1 more source

Spectral and Scattering Theory for the Laplacian on Asymptotically Euclidian Spaces

Spectral and scattering theory, 2020
There are many approaches to conventional Euclidian scattering theory. In this exposition an essentially microlocal view is adopted. Apart from its intrinsic interest this is intended as preparation for later generalization, to more complicated geometric
R. Melrose
semanticscholar   +1 more source

Laplacians and Sub-Laplacians [PDF]

, 2010
We have seen in the previous chapters how an elliptic operator can be associated in a natural way with a geometric Riemannian structure. In a similar way sub-elliptic operators arise from similar structures, called sub-Riemannian structures, which will be discussed next. References for sub-Riemannian manifolds are [27] and [92].
Chisato Iwasaki, Der-Chen Chang, Ovidiu Calin, Kenro Furutani   +3 more
openaire   +1 more source

The Laplacian Pyramid as a Compact Image Code

IEEE Transactions on Communications, 1983
We describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions. The representation differs from established techniques in that the code elements are localized in spatial frequency as ...
P. Burt, E. Adelson
semanticscholar   +1 more source

Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering

Neural Information Processing Systems, 2001
Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low
M. Belkin, P. Niyogi
semanticscholar   +1 more source

Hyper-Laplacian Regularized Multilinear Multiview Self-Representations for Clustering and Semisupervised Learning

IEEE Transactions on Cybernetics, 2020
In this paper, we address the multiview nonlinear subspace representation problem. Traditional multiview subspace learning methods assume that the heterogeneous features of the data usually lie within the union of multiple linear subspaces.
Yuan Xie, Wensheng Zhang, Yanyun Qu, Longquan Dai, D. Tao   +4 more
semanticscholar   +1 more source