# Deep Reconstruction Models The simplest method for reconstructing an image from measurements is to pass it through a feedforward model architecture that is conditioned on the acquisition physics, that is $\inversef{y}{A}$. We offer a range of architectures for general and specific problems. ## Artifact Removal The simplest reconstruction architecture first maps the measurements to the image domain via a non-learned mapping, and then applies a denoiser network to the obtain the final reconstruction. The [`deepinv.models.ArtifactRemoval`](https://deepinv.org/api/stubs/deepinv.models.ArtifactRemoval.html.md#deepinv.models.ArtifactRemoval) class converts a denoiser [`deepinv.models.Denoiser`](https://deepinv.org/api/stubs/deepinv.models.Denoiser.html.md#deepinv.models.Denoiser) or other image-to-image network $\phi$ into a reconstruction network [`deepinv.models.Reconstructor`](https://deepinv.org/api/stubs/deepinv.models.Reconstructor.html.md#deepinv.models.Reconstructor) $R$ by doing - Adjoint: $\inversef{y}{A}=\phi(A^{\top}y)$ with `mode='adjoint'`.
This option is generally to linear operators $A$.
- Pseudoinverse: $\inversef{y}{A}=\phi(A^{\dagger}y)$ with `mode='pinv'`. - Direct: $\inversef{y}{A}=\phi(y)$ with `mode='direct'`.
This option serves as a wrapper to obtain a [`Reconstructor`](https://deepinv.org/api/stubs/deepinv.models.Reconstructor.html.md#deepinv.models.Reconstructor), and can be used to adapt a generic denoiser or image-to-image network into one that is specific to an inverse problem.
## General reconstruction models We provide the following list of reconstruction models trained on multiple various physics and datasets to provide robustness to different problems. See [Description of weights](https://deepinv.org/user_guide/reconstruction/pretrained-models.html.md#pretrained-weights) for more information on pretrained denoisers. #### Multiphysics reconstruction models | Model | Type | Tensor Size (C, H, W) | Pretrained Weights | Noise level aware | |----------------------------------------------------------------------------------------|----------|-------------------------|----------------------|---------------------| | [`deepinv.models.RAM`](https://deepinv.org/api/stubs/deepinv.models.RAM.html.md#deepinv.models.RAM) | CNN-UNet | C=1, 2, 3; H,W>8 | C=1, 2, 3 | Yes | ## Specific reconstruction models We also provide some architectures for specific inverse problems. #### Specific architectures | Model | Description | |--------------------------------------------------------------------------------------------------|--------------------------------------| | [`deepinv.models.PanNet`](https://deepinv.org/api/stubs/deepinv.models.PanNet.html.md#deepinv.models.PanNet) | PanNet model for pansharpening. | | [`deepinv.models.SRResNet`](https://deepinv.org/api/stubs/deepinv.models.SRResNet.html.md#deepinv.models.SRResNet) | SRResNet model for super-resolution. |