Light-weight residual learning for single image dehazing

Jing Ding; Zhigang Yan; Xuchen Wei; Xiaoshun Li

doi:10.1117/1.JEI.28.3.033013

23 May 2019 Light-weight residual learning for single image dehazing

Jing Ding, Zhigang Yan, Xuchen Wei, Xiaoshun Li

Author Affiliations +

Funded by: National Natural Science Foundation of China (NSFC), Open Fund for the Key Laboratory for Coastal Zone Development and Protection of the Ministry of Land and Resources, National Key Research and Development Program of China

Journal of Electronic Imaging, Vol. 28, Issue 3, 033013 (May 2019). https://doi.org/10.1117/1.JEI.28.3.033013

Abstract

Images captured on a hazy day are often degraded by the particle matter suspended in air, which introduces haze that severely distorts the quality of the acquired data. Eliminating haze, therefore, is a critical task in real-world applications. We propose a deep light-weight residual model, called image dehazing deep fully convolutional network (ID-DFCN), based on residual learning that directly projects the given hazy image to the residual onto the hazy image and the corresponding haze-free image. As a result, the haze-free image is obtained by adding the estimated residual. The proposed ID-DFCN is an end-to-end and light-weight model, which enables efficient hardware implementation. Qualitative and quantitative evaluations on synthetic and real-world hazy images show that the proposed model achieves comparable and even superior results in comparison to several state-of-the-art methods.

Citation Download Citation

Jing Ding, Zhigang Yan, Xuchen Wei, and Xiaoshun Li "Light-weight residual learning for single image dehazing," Journal of Electronic Imaging 28(3), 033013 (23 May 2019). https://doi.org/10.1117/1.JEI.28.3.033013

Received: 30 December 2018; Accepted: 2 May 2019; Published: 23 May 2019

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