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As a result, the approach can even provide new insight into the behavior of model-based linear diffusion estimation methods in contexts where the modeling assumptions are inaccurate.
The new framework can be used to characterize arbitrary linear diffusion estimation methods with arbitrary q-space sampling, and can be used to theoretically evaluate and compare the accuracy, resolution, and noise-resilience of different data acquisition and parameter estimation techniques.
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For example, in [2], Eva and Aishy used the Gaussian filter for image de-noising where they studied the relation between Gaussian scale space and linear diffusion, and they derived an estimation of the Gaussian variance and window size; this technique is called AGSS.
At macroelectrodes, linear diffusion dominates, resulting in limited mass transport.
They used an ingenious methodology, called the linear diffusion chain, to model the transmission and modification of caricatures over generations.
Proponents of linear diffusion would argue that people were not aware of the better keyboard or did not have enough knowledge to understand its benefits.
Note that the space-dependent part (5) is simply a linear diffusion equation.
In the next section, we will focus on the space discretization of the linear diffusion equation.
It produces much more accurate diffusion estimations for non-constant diffusion.
Hence (1.3) is a non-linear diffusion equation for s.
The surface areas are inpainted using a linear or non-linear diffusion process.
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