Denoising RENOIR Image Dataset with DBSR

Fatma Albluwi; Vladimir A. Krylov; Rozenn Dahyot

doi:10.21427/g34k-8r27

Denoising RENOIR Image Dataset with DBSR

Fatma Albluwi
, Vladimir A. Krylov
, Rozenn Dahyot

Research output: Contribution to conference › Paper › peer-review

Abstract

Noise reduction algorithms have often been evaluated using images degraded by artificially synthesised noise. The RENOIR image dataset [3] provides an alternative way for testing noise reduction algorithms on real noisy images and we propose in this paper to assess our CNN called De-Blurring Super-Resolution (DBSR) [2] to reduce the natural noise due to low light conditions in a RENOIR dataset.

Original language	English
DOIs	https://doi.org/10.21427/g34k-8r27
Publication status	Published - 1 Jan 2019
Externally published	Yes
Event	IMVIP 2019: Irish Machine Vision & Image Processing - Technological University Dublin, Dublin, Ireland Duration: 28 Aug 2019 → 30 Aug 2019

Conference

Conference	IMVIP 2019: Irish Machine Vision & Image Processing
Country/Territory	Ireland
City	Dublin
Period	28/08/19 → 30/08/19

Keywords

Noise reduction
RENOIR image dataset
CNN
De-Blurring Super-Resolution
natural noise
low light conditions

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10.21427/g34k-8r27Licence: CC BY-NC-SA

Cite this

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title = "Denoising RENOIR Image Dataset with DBSR",

abstract = "Noise reduction algorithms have often been evaluated using images degraded by artificially synthesised noise. The RENOIR image dataset [3] provides an alternative way for testing noise reduction algorithms on real noisy images and we propose in this paper to assess our CNN called De-Blurring Super-Resolution (DBSR) [2] to reduce the natural noise due to low light conditions in a RENOIR dataset.",

keywords = "Noise reduction, RENOIR image dataset, CNN, De-Blurring Super-Resolution, natural noise, low light conditions",

author = "Fatma Albluwi and Krylov, \{Vladimir A.\} and Rozenn Dahyot",

year = "2019",

month = jan,

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doi = "10.21427/g34k-8r27",

language = "English",

note = "IMVIP 2019: Irish Machine Vision \& Image Processing ; Conference date: 28-08-2019 Through 30-08-2019",

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T1 - Denoising RENOIR Image Dataset with DBSR

AU - Albluwi, Fatma

AU - Krylov, Vladimir A.

AU - Dahyot, Rozenn

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Noise reduction algorithms have often been evaluated using images degraded by artificially synthesised noise. The RENOIR image dataset [3] provides an alternative way for testing noise reduction algorithms on real noisy images and we propose in this paper to assess our CNN called De-Blurring Super-Resolution (DBSR) [2] to reduce the natural noise due to low light conditions in a RENOIR dataset.

AB - Noise reduction algorithms have often been evaluated using images degraded by artificially synthesised noise. The RENOIR image dataset [3] provides an alternative way for testing noise reduction algorithms on real noisy images and we propose in this paper to assess our CNN called De-Blurring Super-Resolution (DBSR) [2] to reduce the natural noise due to low light conditions in a RENOIR dataset.

KW - Noise reduction

KW - RENOIR image dataset

KW - CNN

KW - De-Blurring Super-Resolution

KW - natural noise

KW - low light conditions

U2 - 10.21427/g34k-8r27

DO - 10.21427/g34k-8r27

M3 - Paper

T2 - IMVIP 2019: Irish Machine Vision & Image Processing

Y2 - 28 August 2019 through 30 August 2019

ER -

Denoising RENOIR Image Dataset with DBSR

Abstract

Conference

Keywords

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