文献类型：会议论文

英文题名：ODCR: Orthogonal Decoupling Contrastive Regularization for Unpaired Image Dehazing

作者：Wang, Zhongze[1];Zhao, Haitao[1];Peng, Jingchao[1];Yao, Lujian[1];Zhao, Kaijie[1]

机构：[1]East China Univ Sci & Technol, Shanghai, Peoples R China

会议论文集：IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

会议日期：JUN 16-22, 2024

会议地点：Seattle, WA

语种：英文

摘要：Unpaired image dehazing (UID) holds significant research importance due to the challenges in acquiring haze/clear image pairs with identical backgrounds. This paper proposes a novel method for UID named Orthogonal Decoupling Contrastive Regularization (ODCR). Our method is grounded in the assumption that an image consists of both haze-related features, which influence the degree of haze, and haze-unrelated features, such as texture and semantic information. ODCR aims to ensure that the haze-related features of the dehazing result closely resemble those of the clear image, while the haze-unrelated features align with the input hazy image. To accomplish the motivation, Orthogonal MLPs optimized geometrically on the Stiefel manifold are proposed, which can project image features into an orthogonal space, thereby reducing the relevance between different features. Furthermore, a task-driven Depth-wise Feature Classifier ( DWFC) is proposed, which assigns weights to the orthogonal features based on the contribution of each channel's feature in predicting whether the feature source is hazy or clear in a self-supervised fashion. Finally, a Weighted PatchNCE (WP-NCE) loss is introduced to achieve the pulling of haze-related features in the output image toward those of clear images, while bringing haze-unrelated features close to those of the hazy input. Extensive experiments demonstrate the superior performance of our ODCR method on UID.