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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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -