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 (WPNCE) 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.

本文提出了一种名为 ODCR 的 UID 新方法，该方法基于图像包含影响雾度的与雾无关的特征，通过在 Stiefel 流形上优化的正交多层感知机将图像特征投影到正交空间，使得去雾结果中的与雾相关特征与清晰图像中的特征类似，而与雾无关特征与输入有雾图像中的特征相近，同时引入了 DWFC 任务驱动的深度特征分类器和 WPNCE 加权补丁负例（Weighted PatchNCE）损失来优化去雾结果。实验证明 ODCR 方法在 UID 上具有优越性能。