gaussian_blur#

deepinv.physics.blur.gaussian_blur(sigma=(1, 1), angle=0)[source]#

Gaussian blur filter.

Defined as

\[\begin{equation*} G(x, y) = \frac{1}{2\pi\sigma_x\sigma_y} \exp{\left(-\frac{x'^2}{2\sigma_x^2} - \frac{y'^2}{2\sigma_y^2}\right)} \end{equation*}\]

where \(x'\) and \(y'\) are the rotated coordinates obtained by rotating $(x, y)$ around the origin by an angle \(\theta\):

\[\begin{split}\begin{align*} x' &= x \cos(\theta) - y \sin(\theta) \\ y' &= x \sin(\theta) + y \cos(\theta) \end{align*}\end{split}\]

with \(\sigma_x\) and \(\sigma_y\) the standard deviations along the \(x'\) and \(y'\) axes.

Parameters:
  • sigma (float, tuple[float]) – standard deviation of the gaussian filter. If sigma is a float the filter is isotropic, whereas if sigma is a tuple of floats (sigma_x, sigma_y) the filter is anisotropic.

  • angle (float) – rotation angle of the filter in degrees (only useful for anisotropic filters)

Examples using gaussian_blur:#

Use a pretrained model

Use a pretrained model

5 minute quickstart tutorial

5 minute quickstart tutorial

Image deblurring with custom deep explicit prior.

Image deblurring with custom deep explicit prior.

Tour of blur operators

Tour of blur operators

Tour of forward sensing operators

Tour of forward sensing operators

Plug-and-Play algorithm with Mirror Descent for Poisson noise inverse problems.

Plug-and-Play algorithm with Mirror Descent for Poisson noise inverse problems.