Gradient Guided Dual-Branch Network for Image Dehazing

Abstract

Recently, massive deep learning-based image dehazing methods have sprung up. These methods can effectively remove most of the haze and obtain far better results than the traditional methods. With the removal of the haze, however, edge details of the image are also lost, which is usually more noticeable in the gradient space. This paper proposes a gradient guided dual-branch network (GGDB-Net) for image dehazing. Specifically, we explore the hazy image gradient map to guide our model to focus on the hazy regions and edge restoration. We implement two parallel branches with a comprehensive loss function, which collaborate to dehaze and repair the lost edges in haze images. Moreover, the gradient-guided approach can potentially be applied to existing learning-based image dehazing models to boost their performance. Experimental results indicate that our results have good visual perceptions and are comparable to state-of-the-art methods in quantitative metrics.