Polarized Images-Based Dehazing From the Viewpoint of Self-Guided Multi-Image Features Fusion

Different from single image dehazing, polarized images can record richer scene information, and thus polarized images-based dehazing has attracted tremendous attention recently. Existing polarized images-based dehazing methods usually propose some priors or assumptions to calculate the degree of polarization or angle of polarization for dehazing. Although these methods have made significant progress, the priors or assumptions are not always reliable in the real scene, which limits their performance. In addition, most existing methods only consider pixel-level difference information and ignore extracting more effective information from themselves. Based on the above analysis, we propose a novel framework that transforms the polarized images-based dehazing problem into a multi-image fusion problem without any assumption based on the fact that the source of the polarized image is from the same scene but contains different scene information. Specifically, we first pre-train a generic dehazing physical model to obtain an intermediate result that serves as a reference image due to a clearer structure. Then the reference image is used to guide the features fusion to extract more effective features from the input polarized images themselves. Extensive experimental results show that the proposed method achieves superior performance against state-of-the-art methods on both synthetic and real data.