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Image Restoration using Plug-and-Play CNN MAP Denoisers

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Siavash Bigdeli, David Honzátko, Sabine Süsstrunk, L. Andrea Dunbar

Image and Video Processing · Submitted 18 Dec 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Our experimental results show that the proposed method can achieve 70x faster performance compared to the state-of-the-art, while maintaining the theoretical perspective of MAP.

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arXiv ID1912.09299 (version 2)
Pith IDC3DJJVBU, Pith's permanent record of this paper and its review
CitationDavid Honz\'atko, L. Andrea Dunbar, Sabine S\"usstrunk, Siavash Bigdeli. "Image Restoration using Plug-and-Play CNN MAP Denoisers." arXiv:1912.09299 (2019). https://pith.science/paper/C3DJJVBU
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,842 input tokens.

What the abstract itself states, as probabilities: claims first 0.96 · states limitation 0.07 · human participants 0.03 · controlled comparison 0.55 · randomized assignment 0.01 · states quantitative result 0.98 · compares with prior results 0.92 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

Plug-and-play denoisers can be used to perform generic image restoration tasks independent of the degradation type. These methods build on the fact that the Maximum a Posteriori (MAP) optimization can be solved using smaller sub-problems, including a MAP denoising optimization. We present the first end-to-end approach to MAP estimation for image denoising using deep neural networks. We show that our method is guaranteed to minimize the MAP denoising objective, which is then used in an optimization algorithm for generic image restoration. We provide theoretical analysis of our approach and show the quantitative performance of our method in several experiments. Our experimental results show that the proposed method can achieve 70x faster performance compared to the state-of-the-art, while maintaining the theoretical perspective of MAP.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Plug-and-play denoisers can be used to perform generic image restoration tasks independent of the degradation type. background 1.00 0.07 0.07 asserted 0.61
s1 These methods build on the fact that the Maximum a Posteriori (MAP) optimization can be solved using smaller sub-problems, including a MAP denoising optimization. background 0.99 0.06 0.04 asserted 1.00
s2 We present the first end-to-end approach to MAP estimation for image denoising using deep neural networks. contribution 1.00 0.93 0.22 asserted 1.00
s3 We show that our method is guaranteed to minimize the MAP denoising objective, which is then used in an optimization algorithm for generic image restoration. result 0.60 0.94 0.39 asserted 1.00
s4 We provide theoretical analysis of our approach and show the quantitative performance of our method in several experiments. contribution 0.91 0.94 0.07 asserted 0.99
s5 Our experimental results show that the proposed method can achieve 70x faster performance compared to the state-of-the-art, while maintaining the theoretical perspective of MAP. result 1.00 0.95 0.44 asserted 0.97

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.89.

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  • full text
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  • references and citing sentences
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  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Plug-and-play denoisers can be used to perform generic image restoration tasks independent of the degradation type. These methods build on the fact that the Maximum a Posteriori (MAP) optimization can be solved using smaller sub-problems, including a MAP denoising optimization. We present the first end-to-end approach to MAP estimation for image denoising using deep neural networks. We show that our method is guaranteed to minimize the MAP denoising objective, which is then used in an optimization algorithm for generic image restoration. We provide theoretical analysis of our approach and show the quantitative performance of our method in several experiments. Our experimental results show that the proposed method can achieve 70x faster performance compared to the state-of-the-art, while maintaining the theoretical perspective of MAP.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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