Semi-Supervised Image Dehazing

Lerenhan Li, Yunlong Dong, Wenqi Ren, Jinshan Pan, Changxin Gao, Nong Sang, Ming Hsuan Yang

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

We present an effective semi-supervised learning algorithm for single image dehazing. The proposed algorithm applies a deep Convolutional Neural Network (CNN) containing a supervised learning branch and an unsupervised learning branch. In the supervised branch, the deep neural network is constrained by the supervised loss functions, which are mean squared, perceptual, and adversarial losses. In the unsupervised branch, we exploit the properties of clean images via sparsity of dark channel and gradient priors to constrain the network. We train the proposed network on both the synthetic data and real-world images in an end-To-end manner. Our analysis shows that the proposed semi-supervised learning algorithm is not limited to synthetic training datasets and can be generalized well to real-world images. Extensive experimental results demonstrate that the proposed algorithm performs favorably against the state-of-The-Art single image dehazing algorithms on both benchmark datasets and real-world images.

Original languageEnglish
Article number8902220
Pages (from-to)2766-2779
Number of pages14
JournalIEEE Transactions on Image Processing
Volume29
DOIs
Publication statusPublished - 2020

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

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    Li, L., Dong, Y., Ren, W., Pan, J., Gao, C., Sang, N., & Yang, M. H. (2020). Semi-Supervised Image Dehazing. IEEE Transactions on Image Processing, 29, 2766-2779. [8902220]. https://doi.org/10.1109/TIP.2019.2952690