UDPNet: Unleashing Depth-based Priors for Robust Image Dehazing

TL;DR

UDPNet introduces a novel depth-aware framework for image dehazing that effectively integrates depth priors from pretrained models, significantly improving performance across multiple datasets and scenarios.

Contribution

The paper proposes UDPNet, a framework that leverages depth priors with novel modules for hierarchical fusion and attention, enhancing dehazing results beyond existing methods.

Findings

Outperforms state-of-the-art on multiple datasets

Achieves PSNR improvements of up to 1.79 dB

Demonstrates robustness across synthetic and real-world data

Abstract

Image dehazing has witnessed significant advancements with the development of deep learning models. However, most existing methods focus solely on single-modal RGB features, neglecting the inherent correlation between scene depth and haze distribution. Even those that jointly optimize depth estimation and image dehazing often suffer from suboptimal performance due to inadequate utilization of accurate depth information. In this paper, we present UDPNet, a general framework that leverages depth-based priors from a large-scale pretrained depth estimation model DepthAnything V2 to boost existing image dehazing models. Specifically, our architecture comprises two key components: the Depth-Guided Attention Module (DGAM) adaptively modulates features via lightweight depth-guided channel attention, and the Depth Prior Fusion Module (DPFM) enables hierarchical fusion of multi-scale depth map features by dual sliding-window multi-head cross-attention mechanism. These modules ensure both computational efficiency and effective integration of depth priors. Moreover, the depth priors empower the network to dynamically adapt to varying haze densities, illumination conditions, and domain gaps across synthetic and real-world data. Extensive experimental results demonstrate the effectiveness of our UDPNet, outperforming the state-of-the-art methods on popular dehazing datasets, with PSNR improvements of 0.85 dB on SOTS-indoor, 1.19 dB on Haze4K, and 1.79 dB on NHR. Our proposed solution establishes a new benchmark for depth-aware dehazing across various scenarios. Pretrained models and codes are released at our project https://github.com/Harbinzzy/UDPNet.