Prior#

class deepinv.optim.Prior(g=None)[source]#

Bases: Potential

Prior term \(\reg{x}\).

This is the base class for the prior term \(\reg{x}\). As a child class from the Poential class, it comes with methods for computing \(\operatorname{prox}_{g}\) and \(\nabla \regname\). To implement a custom prior, for an explicit prior, overwrite \(\regname\) (do not forget to specify self.explicit_prior = True)

This base class is also used to implement implicit priors. For instance, in PnP methods, the method computing the proximity operator is overwritten by a method performing denoising. For an implicit prior, overwrite grad or prox.

Note

The methods for computing the proximity operator and the gradient of the prior rely on automatic differentiation. These methods should not be used when the prior is not differentiable, although they will not raise an error.

Parameters:: g (Callable) – Prior function \(g(x)\).

Examples using `Prior`:#

Radio interferometric imaging with deepinverse

Image deblurring with custom deep explicit prior.

Saving and loading models

Random phase retrieval and reconstruction methods.

Image deblurring with Total-Variation (TV) prior

Image inpainting with wavelet prior

Patch priors for limited-angle computed tomography

DPIR method for PnP image deblurring.

PnP with custom optimization algorithm (Condat-Vu Primal-Dual)

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

Regularization by Denoising (RED) for Super-Resolution.

Vanilla PnP for computed tomography (CT).

Building your custom sampling algorithm.

Uncertainty quantification with PnP-ULA.

Deep Equilibrium (DEQ) algorithms for image deblurring

Learned Iterative Soft-Thresholding Algorithm (LISTA) for compressed sensing

Learned iterative custom prior

Learned Primal-Dual algorithm for CT scan.

Unfolded Chambolle-Pock for constrained image inpainting

Vanilla Unfolded algorithm for super-resolution

Prior#

Examples using Prior:#

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Examples using `Prior`:#