Single image dehazing using deep neural networks

Hodges, Cameron and Bennamoun, Mohammed and Rahmani, Hossein (2019) Single image dehazing using deep neural networks. Pattern Recognition Letters, 128. pp. 70-77. ISSN 0167-8655

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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.

Item Type:
Journal Article
Journal or Publication Title:
Pattern Recognition Letters
Additional Information:
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
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1707
Subjects:
ID Code:
136181
Deposited By:
Deposited On:
19 Aug 2019 13:50
Refereed?:
Yes
Published?:
Published
Last Modified:
20 Sep 2020 05:41