Facet-based adaptive anisotropic diffusion for image selective smoothing

2007 ◽  
Author(s):  
Guodong Wang ◽  
Nong Sang ◽  
Luxin Yan ◽  
Xubang Shen
Keyword(s):  
Anisotropic Diffusion ◽  
Selective Smoothing

Coupled anisotropic diffusion for image selective smoothing

2006 ◽  
Vol 86 (7) ◽  
pp. 1728-1736 ◽  
Author(s):  
HongGen Luo ◽  
LiMin Zhu ◽  
Han Ding
Keyword(s):  
Anisotropic Diffusion ◽  
Selective Smoothing

An Evaluation of Colour-to-Greyscale Image Conversion by Linear Anisotropic Diffusion and Manual Colour Grading

2019 ◽  
Vol 2019 (1) ◽  
pp. 69-74
Author(s):  
Aldo Barba ◽  
Ivar Farup ◽  
Marius Pedersen
Keyword(s):  
Anisotropic Diffusion ◽  
Motion Pictures ◽  
Current Paper ◽  
Original Image ◽  
Colour Image ◽  
Image Conversion ◽  
Significant Difference ◽  
Greyscale Image

In the paper "Colour-to-Greyscale Image Conversion by Linear Anisotropic Diffusion of Perceptual Colour Metrics", Farup et al. presented an algorithm to convert colour images to greyscale. The algorithm produces greyscale reproductions that preserve detail derived from local colour differences in the original colour image. Such detail is extracted by using linear anisotropic diffusion to build a greyscale reproduction from a gradient of the original image that is in turn calculated using Riemannised colour metrics. The purpose of the current paper is to re-evaluate one of the psychometric experiments for these two methods (CIELAB L* and anisotropic Δ99) by using a flipping method to compare their resulting images instead of the side by side method used in the original evaluation. In addition to testing the two selected algorithms, a third greyscale reproduction was manually created (colour graded) using a colour correction software commonly used to process motion pictures. Results of the psychometric experiment found that when comparing images using the flipping method, there was a statistically significant difference between the anisotropic Δ99 and CIELAB L* conversions that favored the anisotropic method. The comparison between Δ99 conversion and the manually colour graded image also showed a statistically significant difference between them, in this case favoring the colour graded version.

Improved anisotropic diffusion ultrasound image denoising method based on logarithmic compression

2013 ◽  
Vol 32 (11) ◽  
pp. 3218-3220
Author(s):  
Jin YANG ◽  
Zhi-qin LIU ◽  
Yao-bin WANG ◽  
Xiao-ming GAO
Keyword(s):  
Image Denoising ◽  
Anisotropic Diffusion ◽  
Ultrasound Image ◽  
Denoising Method

Anisotropic diffusion at the field scale in a 4-year multi-tracer diffusion and retention experiment – I: Insights from the experimental data

2014 ◽  
Vol 125 ◽  
pp. 373-393 ◽  
Author(s):  
Thomas Gimmi ◽  
Olivier X. Leupin ◽  
Jost Eikenberg ◽  
Martin A. Glaus ◽  
Luc R. Van Loon ◽  
...  
Keyword(s):  
Experimental Data ◽  
Anisotropic Diffusion ◽  
Tracer Diffusion ◽  
Field Scale

scholarly journals Finsler geometry modeling for anisotropic diffusion in Turing patterns

2021 ◽  
Vol 1730 (1) ◽  
pp. 012035
Author(s):  
Hiroshi Koibuchi ◽  
Masahiko Okumura ◽  
Shuta Noro
Keyword(s):  
Anisotropic Diffusion ◽  
Finsler Geometry ◽  
Turing Patterns ◽  
Geometry Modeling

scholarly journals A noninvasive fluorescence imaging-based platform measures 3D anisotropic extracellular diffusion

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Peng Chen ◽  
Xun Chen ◽  
R. Glenn Hepfer ◽  
Brooke J. Damon ◽  
Changcheng Shi ◽  
...  
Keyword(s):  
Anisotropic Diffusion ◽  
Biological Systems ◽  
Three Dimensional ◽  
Molecular Transport ◽  
Light Sheet ◽  
Fundamental Limitations ◽  
Disease Mechanisms ◽  
Dimensional Diffusion ◽  
Compositional Changes ◽  
Diffusion Tensors

AbstractDiffusion is a major molecular transport mechanism in biological systems. Quantifying direction-dependent (i.e., anisotropic) diffusion is vitally important to depicting how the three-dimensional (3D) tissue structure and composition affect the biochemical environment, and thus define tissue functions. However, a tool for noninvasively measuring the 3D anisotropic extracellular diffusion of biorelevant molecules is not yet available. Here, we present light-sheet imaging-based Fourier transform fluorescence recovery after photobleaching (LiFT-FRAP), which noninvasively determines 3D diffusion tensors of various biomolecules with diffusivities up to 51 µm2 s−1, reaching the physiological diffusivity range in most biological systems. Using cornea as an example, LiFT-FRAP reveals fundamental limitations of current invasive two-dimensional diffusion measurements, which have drawn controversial conclusions on extracellular diffusion in healthy and clinically treated tissues. Moreover, LiFT-FRAP demonstrates that tissue structural or compositional changes caused by diseases or scaffold fabrication yield direction-dependent diffusion changes. These results demonstrate LiFT-FRAP as a powerful platform technology for studying disease mechanisms, advancing clinical outcomes, and improving tissue engineering.

scholarly journals Direction-dependent turning leads to anisotropic diffusion and persistence

2021 ◽  
pp. 1-37
Author(s):  
N. LOY ◽  
T. HILLEN ◽  
K. J. PAINTER
Keyword(s):  
Anisotropic Diffusion ◽  
Cell Movement ◽  
Orientation Dependence ◽  
Transport Equations ◽  
Heterogeneous Environment ◽  
Enhanced Diffusion ◽  
Mesoscopic Level

Cells and organisms follow aligned structures in their environment, a process that can generate persistent migration paths. Kinetic transport equations are a popular modelling tool for describing biological movements at the mesoscopic level, yet their formulations usually assume a constant turning rate. Here we relax this simplification, extending to include a turning rate that varies according to the anisotropy of a heterogeneous environment. We extend known methods of parabolic and hyperbolic scaling and apply the results to cell movement on micropatterned domains. We show that inclusion of orientation dependence in the turning rate can lead to persistence of motion in an otherwise fully symmetric environment and generate enhanced diffusion in structured domains.

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