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 15 Summary of pretrained weights#
Model	Weight
`deepinv.models.DnCNN()`	from Learning Maximally Monotone Operators trained on noise level 2.0/255. DnCNN grayscale weights, DnCNN color weights.
	from Learning Maximally Monotone Operators with Lipschitz constraint to ensure approximate firm nonexpansiveness, trained on noise level 2.0/255. Non-expansive DnCNN grayscale weights, Non-expansive DnCNN 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 DRUNet grayscale weights, DRUNet color weights.
	from DPIR, trained on noise levels in [0, 50]/255. DRUNet original grayscale weights, DRUNET original color weights.
`deepinv.models.GSDRUNet()`	weights from Gradient-Step PnP, trained on noise levels in [0, 50]/255. GSDRUNet color weights.
`deepinv.models.SCUNet()`	from SCUNet, trained on images degraded with synthetic realistic noise and camera artefacts. SCUNet 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). DiffUNet weights.
	from Dhariwal and Nichol trained on ImageNet128 (256 hidden channels per layer). weights.
`deepinv.models.EPLLDenoiser()`	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 the deepinverse Restormer HugginfaceHub.