Unsupervised Domain Adaptation Image Dehazing with Contrastive Nearest-Farthest Subspace Distance

Xiaozhe Meng, Yuxin Feng, Zhuo Su, Fan Zhou

Abstract: Haze can cause significant changes in the data domain of the image. Due to domain-shift problem, the model trained on the synthetic haze images has a weak or even invalid dehazing ef-fect on the real data. This paper proposes an unsupervised domain adaptation dehazing method based on the nearest-farthest subspace distance in response to this problem. For the middle convolved features, the matrix singular value de-composition is used to obtain the basis vectors of the support subspace. Narrowing the angle between the basis vectors of natural and synthetic haze domains could reduce the differ-ence between the data domains. In subspace, a newly defined distance with a new penalty term named subspace measure distance is employed to constrain the model. Furthermore, to avoid search blindness of the proposed method in the real data training phase, we propose the nearest-farthest subspace distance inspired by contrastive learning. In addition, we use a new training strategy. Finally, the experimental tests on the synthetic and real images prove the effectiveness of the pro-posed method.