Blind Poissonian image deblurring regularized by a denoiser constraint and deep image prior
type: Item
instance of: scholarly article
title: Blind Poissonian image deblurring regularized by a denoiser constraint and deep image prior
zbMATH Open document ID: 1459.94012
DOI: 10.1155/2020/9483521
author: Yayuan Feng
author: Yu Shi
author: Dianjun Sun
published in: Mathematical Problems in Engineering
publication date: 2020-10-12T00:00:00Z
review text: Summary: The denoising and deblurring of Poisson images are opposite inverse problems. Single image deblurring methods are sensitive to image noise. A single noise filter can effectively remove noise in advance, but it also damages blurred information. To simultaneously solve the denoising and deblurring of Poissonian images better, we learn the implicit deep image prior from a single degraded image and use the denoiser as a regularization term to constrain the latent clear image. Combined with the explicit \(L_0\) regularization prior of the image, the denoising and deblurring model of the Poisson image is established. Then, the split Bregman iteration strategy is used to optimize the point spread function estimation and latent clear image estimation. The experimental results demonstrate that the proposed method achieves good restoration results on a series of simulated and real blurred images with Poisson noise.
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 62H35
Mathematics Subject Classification ID: 68U10
Mathematics Subject Classification ID: 65R32
Mathematics Subject Classification ID: 90C26
zbMATH DE Number: 7259692
describes a project that uses: DnCNN
describes a project that uses: DeepRED
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2020/9483521
OpenAlex ID: W3080537281
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description: scientific article; zbMATH DE number 7259692