Varicolored haze caused by chromatic casts poses haze removal and depth estimation challenges. Recent learning-based depth estimation methods are mainly targeted at dehazing first and estimating depth subsequently from haze-free scenes. This way, the inner connections between colored haze and scene depth are lost. In this paper, we propose a real-time transformer for simultaneous single image Depth Estimation and Haze Removal (DEHRFormer). DEHRFormer consists of a single encoder and two task-specific decoders. The transformer decoders with learnable queries are designed to decode coupling features from the task-agnostic encoder and project them into clean image and depth map, respectively. In addition, we introduce a novel learning paradigm that utilizes contrastive learning and domain consistency learning to tackle weak-generalization problem for real-world dehazing, while predicting the same depth map from the same scene with varicolored haze. Experiments demonstrate that DEHRFormer achieves significant performance improvement across diverse varicolored haze scenes over previous depth estimation networks and dehazing approaches.

翻译：杂色雾霾由色偏引起，给去雾和深度估计带来了挑战。现有的基于学习的深度估计方法主要先进行去雾，再从无雾场景中估计深度。这种方式割裂了有色雾霾与场景深度之间的内在联系。本文提出一种用于同时进行单图像深度估计与去雾的实时Transformer（DEHRFormer）。DEHRFormer包含单一编码器和两个任务专用解码器。设计带有可学习查询的Transformer解码器，从任务无关编码器中解耦特征，并分别将其投影为清晰图像和深度图。此外，我们引入一种利用对比学习和域一致性学习的新型学习范式，以解决真实场景去雾中的弱泛化问题，同时从同一场景的不同颜色雾霾中预测一致的深度图。实验表明，在多种杂色雾霾场景中，DEHRFormer相较于现有深度估计网络和去雾方法均取得了显著的性能提升。