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    Rights statement: This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters, 128, 2019 DOI: 10.1016/j.patrec.2019.08.013

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Single image dehazing using deep neural networks

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Single image dehazing using deep neural networks. / Hodges, Cameron; Bennamoun, Mohammed; Rahmani, Hossein.

In: Pattern Recognition Letters, Vol. 128, 01.12.2019, p. 70-77.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Hodges, C, Bennamoun, M & Rahmani, H 2019, 'Single image dehazing using deep neural networks', Pattern Recognition Letters, vol. 128, pp. 70-77. https://doi.org/10.1016/j.patrec.2019.08.013

APA

Hodges, C., Bennamoun, M., & Rahmani, H. (2019). Single image dehazing using deep neural networks. Pattern Recognition Letters, 128, 70-77. https://doi.org/10.1016/j.patrec.2019.08.013

Vancouver

Hodges C, Bennamoun M, Rahmani H. Single image dehazing using deep neural networks. Pattern Recognition Letters. 2019 Dec 1;128:70-77. https://doi.org/10.1016/j.patrec.2019.08.013

Author

Hodges, Cameron ; Bennamoun, Mohammed ; Rahmani, Hossein. / Single image dehazing using deep neural networks. In: Pattern Recognition Letters. 2019 ; Vol. 128. pp. 70-77.

Bibtex

@article{9914687b066640e2a48e6d6ecf01c59e,
title = "Single image dehazing using deep neural networks",
abstract = "The rapid growth in computer vision applications that are affected by environmental conditions challenge the limitations of existing techniques. This is driving the development of new deep learning based vision techniques that are robust to environmental noise and interference. We propose a novel deep CNN model, which is trained from unmatched images for the purpose of image dehazing. This solution is enabled by the concept of the Siamese network architecture. Using object performance measures of image PSNR and SSIM we are able to demonstrate a quantitative and qualitative improvement in the network dehazing performance. This superior performance is achieved with significantly smaller training datasets than existing methods.",
keywords = "Deep Learing, Siamese Networks, Single Image Dehazing, Image Dehazing",
author = "Cameron Hodges and Mohammed Bennamoun and Hossein Rahmani",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters, 128, 2019 DOI: 10.1016/j.patrec.2019.08.013",
year = "2019",
month = dec,
day = "1",
doi = "10.1016/j.patrec.2019.08.013",
language = "English",
volume = "128",
pages = "70--77",
journal = "Pattern Recognition Letters",
issn = "0167-8655",
publisher = "Elsevier Science B.V.",

}

RIS

TY - JOUR

T1 - Single image dehazing using deep neural networks

AU - Hodges, Cameron

AU - Bennamoun, Mohammed

AU - Rahmani, Hossein

N1 - This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters, 128, 2019 DOI: 10.1016/j.patrec.2019.08.013

PY - 2019/12/1

Y1 - 2019/12/1

N2 - The rapid growth in computer vision applications that are affected by environmental conditions challenge the limitations of existing techniques. This is driving the development of new deep learning based vision techniques that are robust to environmental noise and interference. We propose a novel deep CNN model, which is trained from unmatched images for the purpose of image dehazing. This solution is enabled by the concept of the Siamese network architecture. Using object performance measures of image PSNR and SSIM we are able to demonstrate a quantitative and qualitative improvement in the network dehazing performance. This superior performance is achieved with significantly smaller training datasets than existing methods.

AB - The rapid growth in computer vision applications that are affected by environmental conditions challenge the limitations of existing techniques. This is driving the development of new deep learning based vision techniques that are robust to environmental noise and interference. We propose a novel deep CNN model, which is trained from unmatched images for the purpose of image dehazing. This solution is enabled by the concept of the Siamese network architecture. Using object performance measures of image PSNR and SSIM we are able to demonstrate a quantitative and qualitative improvement in the network dehazing performance. This superior performance is achieved with significantly smaller training datasets than existing methods.

KW - Deep Learing

KW - Siamese Networks

KW - Single Image Dehazing

KW - Image Dehazing

U2 - 10.1016/j.patrec.2019.08.013

DO - 10.1016/j.patrec.2019.08.013

M3 - Journal article

VL - 128

SP - 70

EP - 77

JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

SN - 0167-8655

ER -