Real image denoising with feature attention

Saeed Anwar; Nick Barnes

doi:10.1109/ICCV.2019.00325

Real image denoising with feature attention

Saeed Anwar^*, Nick Barnes

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

551 Citations (Scopus)

Abstract

Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, its performance is limited on real-noisy photographs and requires multiple stage network modeling. To advance the practicability of the denoising algorithms, this paper proposes a novel single-stage blind real image denoising network (RIDNet) by employing a modular architecture. We use residual on the residual structure to ease the flow of low-frequency information and apply feature attention to exploit the channel dependencies. Furthermore, the evaluation in terms of quantitative metrics and visual quality on three synthetic and four real noisy datasets against 19 state-of-the-art algorithms demonstrate the superiority of our RIDNet.

Original language	English
Title of host publication	Proceedings - 2019 International Conference on Computer Vision, ICCV 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3155-3164
Number of pages	10
ISBN (Electronic)	9781728148038
DOIs	https://doi.org/10.1109/ICCV.2019.00325
Publication status	Published - Oct 2019
Event	17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 - Seoul, Korea, Republic of Duration: 27 Oct 2019 → 2 Nov 2019

Publication series

Name	Proceedings of the IEEE International Conference on Computer Vision
Volume	2019-October
ISSN (Print)	1550-5499

Conference

Conference	17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
Country/Territory	Korea, Republic of
City	Seoul
Period	27/10/19 → 2/11/19

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10.1109/ICCV.2019.00325

Cite this

Anwar, S., & Barnes, N. (2019). Real image denoising with feature attention. In Proceedings - 2019 International Conference on Computer Vision, ICCV 2019 (pp. 3155-3164). Article 9010324 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2019-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2019.00325

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title = "Real image denoising with feature attention",

abstract = "Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, its performance is limited on real-noisy photographs and requires multiple stage network modeling. To advance the practicability of the denoising algorithms, this paper proposes a novel single-stage blind real image denoising network (RIDNet) by employing a modular architecture. We use residual on the residual structure to ease the flow of low-frequency information and apply feature attention to exploit the channel dependencies. Furthermore, the evaluation in terms of quantitative metrics and visual quality on three synthetic and four real noisy datasets against 19 state-of-the-art algorithms demonstrate the superiority of our RIDNet.",

author = "Saeed Anwar and Nick Barnes",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 ; Conference date: 27-10-2019 Through 02-11-2019",

year = "2019",

month = oct,

doi = "10.1109/ICCV.2019.00325",

language = "English",

series = "Proceedings of the IEEE International Conference on Computer Vision",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings - 2019 International Conference on Computer Vision, ICCV 2019",

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Anwar, S & Barnes, N 2019, Real image denoising with feature attention. in Proceedings - 2019 International Conference on Computer Vision, ICCV 2019., 9010324, Proceedings of the IEEE International Conference on Computer Vision, vol. 2019-October, Institute of Electrical and Electronics Engineers Inc., pp. 3155-3164, 17th IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea, Republic of, 27/10/19. https://doi.org/10.1109/ICCV.2019.00325

Real image denoising with feature attention. / Anwar, Saeed; Barnes, Nick.
Proceedings - 2019 International Conference on Computer Vision, ICCV 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3155-3164 9010324 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2019-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, its performance is limited on real-noisy photographs and requires multiple stage network modeling. To advance the practicability of the denoising algorithms, this paper proposes a novel single-stage blind real image denoising network (RIDNet) by employing a modular architecture. We use residual on the residual structure to ease the flow of low-frequency information and apply feature attention to exploit the channel dependencies. Furthermore, the evaluation in terms of quantitative metrics and visual quality on three synthetic and four real noisy datasets against 19 state-of-the-art algorithms demonstrate the superiority of our RIDNet.

AB - Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, its performance is limited on real-noisy photographs and requires multiple stage network modeling. To advance the practicability of the denoising algorithms, this paper proposes a novel single-stage blind real image denoising network (RIDNet) by employing a modular architecture. We use residual on the residual structure to ease the flow of low-frequency information and apply feature attention to exploit the channel dependencies. Furthermore, the evaluation in terms of quantitative metrics and visual quality on three synthetic and four real noisy datasets against 19 state-of-the-art algorithms demonstrate the superiority of our RIDNet.

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M3 - Conference contribution

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BT - Proceedings - 2019 International Conference on Computer Vision, ICCV 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 17th IEEE/CVF International Conference on Computer Vision, ICCV 2019

Y2 - 27 October 2019 through 2 November 2019

ER -

Real image denoising with feature attention

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