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Article overview
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Fast Diffusion EM: a diffusion model for blind inverse problems with application to deconvolution | | Charles Laroche
; Andrés Almansa
; Eva Coupete
; | | Date: |
1 Sep 2023 | | Abstract: | Using diffusion models to solve inverse problems is a growing field of
research. Current methods assume the degradation to be known and provide
impressive results in terms of restoration quality and diversity. In this work,
we leverage the efficiency of those models to jointly estimate the restored
image and unknown parameters of the degradation model. In particular, we
designed an algorithm based on the well-known Expectation-Minimization (EM)
estimation method and diffusion models. Our method alternates between
approximating the expected log-likelihood of the inverse problem using samples
drawn from a diffusion model and a maximization step to estimate unknown model
parameters. For the maximization step, we also introduce a novel blur kernel
regularization based on a Plug & Play denoiser. Diffusion models are long to
run, thus we provide a fast version of our algorithm. Extensive experiments on
blind image deblurring demonstrate the effectiveness of our method when
compared to other state-of-the-art approaches. | | Source: | arXiv, 2309.00287 | | Services: | Forum | Review | PDF | Favorites | |
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