Knowledge Transfer Dehazing Network for NonHomogeneous Dehazing
Haiyan Wu, Jing Liu, Yuan Xie, Yanyun Qu, Lizhuang Ma
Abstract
Single image dehazing is an ill-posed problem that has recently drawn important attention. It is a challenging image process task, especially in nonhomogeneous scene. However, the existing dehazing methods are commonly designed to handle homogeneous haze which is easily violated in practice, due to the unknown haze distribution of real world. In this paper, we propose a knowledge transfer method that utilizes abundant clear images to train a teacher network to provide strong and robust image prior. The derived architecture is referred to as the Knowledge Transform Dehaze Network (KTDN), which consists of the teacher network and the dehazing network with identical architecture. Through the supervision between intermediate features, the dehazing network is encouraged to imitate the teacher network. In addition, we use attention mechanism to combine channel attention with pixel attention to capture effective information, and employ an enhancing module to refine detail textures. Extensive experimental results on synthetic and real scene datasets demonstrates that the proposed method outperforms the state-of-the-arts in both quantitative and qualitative evaluations. The KTDN ranks 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> in NTIRE-2020 NonHomogeneous Dehazing Challenge [4], [5].