Variational Methods | PDE | Diffusion | Perona-Malik | Denoising | Grad Desc | Tikhonov | TV | ROF
Variational Methods (Calculus of Variation) in Image Processing and Computer Vision: using PDEs (Partial Differential Equations) and steepest (gradient) descent to solve problems (with Von Neumann and Dirichlet Boundary conditions) - an implementation in python
Diffusion: compare isotropic and non-linear (inhomogenous) Perona-Malik Diffusion techniques preserving edges (for different nonlonear diffusivity functions and with different conductivity values), can be used for texture-flattening / generate cartoonish images
Denoising: Use Euler-Lagrange to obtain PDEs for Gradient Descent update with Tikhonov (L2) vs. TV (L1) regularization (aka the ROF model) which again flattens the texture, along with an additional data fidelity term in the variational minimization objective functional.
#imageprocessing #imageprocessingpython #python #algorithm #machinelearning #computervision #optimization #variatonal #euler #lagrange #dirichlet #neumann #math #calculus
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