  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.
