Denoisers

Denoisers are torch.nn.Module that take a noisy image as input and return a denoised image. They can be used as a building block for plug-and-play restoration, for building unrolled architectures, or as a standalone denoiser. All denoisers have a forward method that takes a noisy image and a noise level (which generally corresponds to the standard deviation of the noise) as input and returns a denoised image:

>>> import torch
>>> import deepinv as dinv
>>> denoiser = dinv.models.DRUNet()
>>> sigma = 0.1
>>> image = torch.ones(1, 3, 32, 32) * .5
>>> noisy_image =  image + torch.randn(1, 3, 32, 32) * sigma
>>> denoised_image = denoiser(noisy_image, sigma)

Note

Some denoisers (e.g., deepinv.models.DnCNN) do not use the information about the noise level. In this case, the noise level is ignored.

Classical Denoisers

`deepinv.models.BM3D`	BM3D denoiser.
`deepinv.models.MedianFilter`	Median filter.
`deepinv.models.TVDenoiser`	Proximal operator of the isotropic Total Variation operator.
`deepinv.models.TGVDenoiser`	Proximal operator of (2nd order) Total Generalised Variation operator.
`deepinv.models.WaveletDenoiser`	Orthogonal Wavelet denoising with the \(\ell_1\) norm.
`deepinv.models.WaveletDictDenoiser`	Overcomplete Wavelet denoising with the \(\ell_1\) norm.
`deepinv.models.EPLLDenoiser`	Expected Patch Log Likelihood denoising method.

Deep Denoisers

`deepinv.models.AutoEncoder`	Simple fully connected autoencoder network.
`deepinv.models.UNet`	U-Net convolutional denoiser.
`deepinv.models.DnCNN`	DnCNN convolutional denoiser.
`deepinv.models.DRUNet`	DRUNet denoiser network.
`deepinv.models.SCUNet`	SCUNet denoising network.
`deepinv.models.GSDRUNet`	Gradient Step Denoiser with DRUNet architecture
`deepinv.models.SwinIR`	SwinIR denoising network.
`deepinv.models.DiffUNet`	Diffusion UNet model.
`deepinv.models.Restormer`	Restormer denoiser network.
`deepinv.models.ICNN`	Input Convex Neural Network.

Equivariant Denoisers

The denoisers can be turned into equivariant denoisers by wrapping them with the deepinv.models.EquivariantDenoiser class, which symmetrizes the denoiser with respect to a transform from our available transforms such as deepinv.transform.Rotate or deepinv.transform.Reflect. You retain full flexibility by passing in the transform of choice.

The denoising can either be averaged over the entire group of transformation (making the denoiser equivariant) or performed on 1 or n transformations sampled uniformly at random in the group, making the denoiser a Monte-Carlo estimator of the exact equivariant denoiser.

deepinv.models.EquivariantDenoiser

Turns the input denoiser into an equivariant denoiser with respect to geometric transforms.

Adversarial Networks

Discriminator networks used in networks trained with adversarial learning using adversarial losses.

`deepinv.models.PatchGANDiscriminator`	PatchGAN Discriminator model.
`deepinv.models.ESRGANDiscriminator`	ESRGAN Discriminator.
`deepinv.models.DCGANGenerator`	DCGAN Generator.
`deepinv.models.DCGANDiscriminator`	DCGAN Discriminator.
`deepinv.models.CSGMGenerator`	Adapts a generator model backbone (e.g DCGAN) for CSGM or AmbientGAN.

Complex Denoisers

Most denoisers in the library are designed to process real images. However, some problems, e.g., phase retrieval, require processing complex-valued images.The function deepinv.models.complex.to_complex_denoiser can convert any real-valued denoiser into a complex-valued denoiser. It can be simply called by complex_denoiser = to_complex_denoiser(denoiser).

deepinv.models.complex.to_complex_denoiser

Converts a denoiser with real inputs into the one with complex inputs.

Pretrained Weights

The following denoisers have pretrained weights available; we next briefly summarize the origin of the weights, associated reference and relevant details. All pretrained weights are hosted on HuggingFace.

Table 1 Summary of pretrained weights
Model	Weight
`deepinv.models.DnCNN()`	from Learning Maximally Monotone Operators trained on noise level 2.0/255. grayscale weights, color weights.
	from Learning Maximally Monotone Operators with Lipschitz constraint to ensure approximate firm nonexpansiveness, trained on noise level 2.0/255. grayscale weights, color weights.
`deepinv.models.DRUNet()`	Default: trained with deepinv (logs), trained on noise levels in [0, 20]/255 and on the same dataset as DPIR grayscale weights, color weights.
	from DPIR, trained on noise levels in [0, 50]/255. grayscale weights, color weights.
`deepinv.models.GSDRUNet()`	weights from Gradient-Step PnP, trained on noise levels in [0, 50]/255. color weights.
`deepinv.models.SCUNet()`	from SCUNet, trained on images degraded with synthetic realistic noise and camera artefacts. color weights.
`deepinv.models.SwinIR()`	from SwinIR, trained on various noise levels levels in {15, 25, 50}/255, in color and grayscale. The weights are automatically downloaded from the authors’ project page.
`deepinv.models.DiffUNet()`	Default: from Ho et al. trained on FFHQ (128 hidden channels per layer). weights.
	from Dhariwal and Nichol trained on ImageNet128 (256 hidden channels per layer). weights.
`deepinv.models.EPLL()`	Default: parameters estimated with deepinv on 50 mio patches from the training/validation images from BSDS500 for grayscale and color images.
	Code for generating the weights for the example Patch priors for limited-angle computed tomography is contained within the demo
`deepinv.models.Restormer()`	from Restormer: Efficient Transformer for High-Resolution Image Restoration. Pretrained parameters from swz30 github.
	Also available on Deepinv Restormer HugginfaceHub.