A Sea Fog Image Defogging Method Based on the Improved Convex Optimization Model

Abstract

Due to the high fog concentration in sea fog images, serious loss of image details is an existing problem, which reduces the reliability of aerial visual-based sensing platforms such as unmanned aerial vehicles. Moreover, the reflection of water surface and spray can easily lead to overexposure of images, and the assumed prior conditions contained in the traditional fog removal method are not completely valid, which affects the restoration effectiveness. In this paper, we propose a sea fog removal method based on the improved convex optimization model, and realize the restoration of images by using fewer prior conditions than that in traditional methods. Compared with dark channel methods, the solution of atmospheric light estimation is simplified, and the value channel in hue–saturation–value space is used for fusion atmospheric light map estimation. We construct the atmospheric scattering model as an improved convex optimization model so that the relationship between the transmittance and a clear image is deduced without any prior conditions. In addition, an improved split-Bregman iterative method is designed to obtain the transmittance and a clear image. Our experiments demonstrate that the proposed method can effectively defog sea fog images. Compared with similar methods in the literature, our proposed method can actively extract image details more effectively, enrich image color and restore image maritime targets more clearly. At the same time, objective metric indicators such as information entropy, average gradient, and the fog-aware density evaluator are significantly improved.