Visible light polarization image desmogging via Cycle Convolutional Neural Network.

Visible light polarization image desmogging aims to recover the clear image solely from an input visible light polarization image with smog. It is a challenging research topic due to desmogging is an ill-posed problem. Most of existing methods effort to build desmogging physical model based on classical atmospheric scattering principle. However, compared with natural image dehazing task, the smog in visible light polarization image is very complex and diverse, which makes it hard to find accurate desmogging physical model. To address this issue, we propose a Cycle Convolutional Neural Network (CCNN), which exploits polarization characteristics explicitly to learn a visible light polarization image desmogging model in an end-to-end way. To be specific, the model computes polarization information of the visible light polarization image via Stokes equation. Object detection sub-network is utilized to detect smog regions according to the polarization information. Then an encoder–decoder sub-network with feature converter structure is proposed to generate smog-free regions. The coarse clear image is obtained by fusing the generated smog-free regions with original smog visible light polarization image. More importantly, to obtain the final clear image, the coarse clear image is considered as the input data to our model again, which makes our model in a cycle topology. Moreover, we contribute the first large-scale dataset for visible light polarization image desmogging evaluation, which contains 17,216 visible light polarization images, to validate our proposed method. On this dataset, extensive experiments demonstrate that our method can achieve the best performance in comparison with the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]