JFMNet: Joint Fusion Multi-Networks for Image Dehazing and Denoising in The Port Environment

Abstract

The bad weather events, such as haze, in maritime traffic dramatically reduce the visibility, which can seriously affect the ship navigation especially in areas with intensive port traffic. Meanwhile, unwanted signals are inevitably introduced by the maritime imaging device during image capturing and transmission in hazy conditions. Therefore, the captured image is not only degraded by the haze, but also may contain unwanted noise. These low-quality images interfere with the subsequent image processing and increase the potential for maritime traffic accidents. It is therefore imperative to improve the image quality in hazy conditions. To reveal the information hidden in the haze while suppress noise, this paper proposes the joint fusion multi-networks (termed JFMNet) for Image dehazing and denoising in the port environment. The multi-networks use the dehazing module (DHNet) and the denoising module (DNNet) to suppress the noise and haze. Then use the information fusion module (FNet) to integrate the results of the DNNet and DHNet with the information of the original input images to achieve the goal of dehazing and denoising while preserving the details. The modules in multi-networks are based on an encoder-decoder structure. Experiments on a number of challenging hazy images with noise are present to reveal the efficacy of this structure. Meanwhile, experiments also show our JFMNet's superiority over several state-of-the-arts in terms of dehaze quality and efficiency.