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Generic Model-Agnostic Convolutional Neural Networks for Single Image Dehazing

Haze and smog are among the most common environmental factors impacting image quality and, therefore, image analysis. In this paper, I propose an end-to-end generative method for single image dehazing problem. It is based on fully convolutional network and effective network structures to recognize haze structure in input images and restore clear, haze-free ones. The proposed method is agnostic in the sense that it does not explore the atmosphere scattering model, it makes use of convolutional networks advantage in feature extraction and transfer instead. Somewhat surprisingly, it achieves superior performance relative to all existing state-of-the-art methods for image dehazing even on SOTS outdoor images, which are synthesized using the atmosphere scattering model. In order to improve its weakness in indoor hazy images and enhance the dehazed image's visual quality, a lightweight parallel network is put forward. It employs a different convolution strategy that extracts features with larger reception field to generate a complementary image. With the help of a parallel stream, the fusion of the two outputs performs better in PSNR and SSIM than other methods. / Thesis / Master of Applied Science (MASc)

Identiferoai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/23979
Date January 2018
CreatorsLiu, Zheng
ContributorsChen, Jun, Electrical and Computer Engineering
Source SetsMcMaster University
LanguageEnglish
Detected LanguageEnglish
TypeThesis

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