Strongly non-local gradient-enhanced finite strain elastoplasticity

2003 ◽  
Vol 56 (14) ◽  
pp. 2039-2068 ◽  
Author(s):  
M. G. D. Geers ◽  
R. L. J. M. Ubachs ◽  
R. A. B. Engelen
Keyword(s):  
Finite Strain ◽  
Non Local ◽  
Local Gradient

scholarly journals Optimal graph edge weights driven nlms with multi-layer residual compensation

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Fang Yang ◽  
Xin Chen ◽  
Li Chai
Keyword(s):  
Image Denoising ◽  
Graph Laplacian ◽  
Denoising Method ◽  
Local Means ◽  
Effective Performance ◽  
Non Local ◽  
Local Gradient ◽  
Laplacian Regularization ◽  
Image Details ◽  
Optimal Graph

AbstractNon-local Means (NLMs) play essential roles in image denoising, restoration, inpainting, etc., due to its simple theory but effective performance. However, when the noise increases, the denoising accuracy of NLMs decreases significantly. This paper further develop the NLMs-based denoising method to remove noise with less loss of image details. It is realized by embedding an optimal graph edge weights driven NLMs kernel into a multi-layer residual compensation framework. Unlike the patch similarity-based weights in the traditional NLMs filters, the edge weights derived from the optimal graph Laplacian regularization consider (1) the distance between the target pixel and the candidate pixel, (2) the local gradient and (3) the patch similarity. After defining the weights, the graph-based NLMs kernel is then put into a multi-layer framework. The corresponding primal and residual terms at each layer are finally fused with learned weights to recover the image. Experimental results show that our method is effective and robust, especially for piecewise smooth images.

Sensitivity analysis of a non‐local, gradient enhanced damage model

PAMM ◽  
2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Fabian Guhr ◽  
Franz‐Joseph Barthold ◽  
Andreas Menzel ◽  
Leon Sprave ◽  
Jan Liedmann
Keyword(s):  
Sensitivity Analysis ◽  
Damage Model ◽  
Non Local ◽  
Local Gradient

Multiresolution Non-Local Mean Filter for Infrared Dim and Small Target Background Suppression

2011 ◽  
Vol 135-136 ◽  
pp. 637-642
Author(s):  
Jia Li ◽  
Juan Chang ◽  
Han Lin Qin
Keyword(s):  
Infrared Image ◽  
Curvelet Transform ◽  
Background Suppression ◽  
Small Target ◽  
Least Mean Square ◽  
Multi Scale ◽  
Local Means ◽  
Mean Filter ◽  
Non Local ◽  
Local Gradient

Structured clouds and ground building background suppression are difficult problems for dim and small target detection technique. In this paper, the dim and small target background suppression method based on combined curvelet transform with modified non-local means filter was presented to solve the problem. And local gradient statistics is introduced to boost ability of method which suppresses false by background structure. The innovation was that the curvelet transform was adopted to decompose the input infrared image, which extracts multi-scale and directional detail features of the image. Moreover non-local means filter improved by local gradient characteristic was introduced to suppress background details and enhance target information for suppression background. Compared with two-dimensional least mean square (TDLMS) and modified partial differential equation (MPDE) methods, through visual quality and value index, several groups of experimental results demonstrate that the presented method can suppress complicated background in dim and small target image effectively.

Global solvability and explicit bounds for non-local adhesion models

2017 ◽  
Vol 29 (4) ◽  
pp. 645-684 ◽  
Author(s):  
T. HILLEN ◽  
K. J. PAINTER ◽  
M. WINKLER
Keyword(s):  
Cell Adhesion ◽  
Global Solvability ◽  
Classical Solutions ◽  
Gradient Term ◽  
Cell Matrix ◽  
Mathematical Properties ◽  
Non Local ◽  
Explicit Bounds ◽  
Local Gradient ◽  
Cell Matrix Adhesion

Adhesion between cells and other cells (cell–cell adhesion) or other tissue components (cell–matrix adhesion) is an intrinsically non-local phenomenon. Consequently, a number of recently developed mathematical models for cell adhesion have taken the form of non-local partial differential equations, where the non-local term arises inside a spatial derivative. The mathematical properties of such a non-local gradient term are not yet well understood. Here we use sophisticated estimation techniques to show local and global existence of classical solutions for such examples of adhesion-type models, and we provide a uniform upper bound for the solutions. Further, we discuss the significance of these results to applications in cell sorting and in cancer invasion and support the theoretical results through numerical simulations.

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