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.
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
Plug-and-Play algorithm with Mirror Descent for Poisson noise inverse problems.
Vanilla PnP for computed tomography (CT).
DPIR method for PnP image deblurring.
Regularization by Denoising (RED) for Super-Resolution.
PnP with custom optimization algorithm (Condat-Vu Primal-Dual)
Uncertainty quantification with PnP-ULA.
Building your custom sampling algorithm.
Learned Iterative Soft-Thresholding Algorithm (LISTA) for compressed sensing
Vanilla Unfolded algorithm for super-resolution
Learned iterative custom prior
Deep Equilibrium (DEQ) algorithms for image deblurring
Learned Primal-Dual algorithm for CT scan.
Unfolded Chambolle-Pock for constrained image inpainting