Light-weight residual learning for single image dehazing
Jing Ding, Zhigang Yan, Xuchen Wei, Xiaoshun Li · Journal of Electronic Imaging · 2019
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.