DDR-Net: Haze-Aware Dual-Domain Refinement for Single-Image Dehazing

• URL: http://arxiv.org/abs/2607.11071v1
• Date: Mon, 13 Jul 2026 04:20:22 GMT
• Title: DDR-Net: Haze-Aware Dual-Domain Refinement for Single-Image Dehazing
• Abstract summary: Single-image dehazing aims to recover clear scenes from haze-degraded images.<n>We propose a Haze-Aware Dual-Domain Refinement Network ( DDR-Net) for single-image dehazing.<n> DDR-Net achieves more effective feature representation and reconstruction for haze removal.
• Score: 2.962656508378179
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single-image dehazing aims to recover clear scenes from haze-degraded images. It remains challenging due to the atmospheric scattering and the complexity of real-world haze distributions. Although recent end-to-end networks have achieved promising performance, two issues still limit their effectiveness: insufficient feature refinement at the bottleneck stage and weak local structural representation in encoder-decoder architectures. Thus, we propose a Haze-Aware Dual-Domain Refinement Network (DDR-Net) for single-image dehazing. Our method is built upon three modules: Haze Prior Extractor (HPE) provides multi-scale haze-aware priors by operating directly on downsampled hazy images; Detail-Enhanced Blocks (DE Blocks) serve as the core feature extraction units, capturing multi-scale structural information and enhancing edge and texture recovery via gradient-aware convolutions; and Spatial-Frequency Bottleneck Refinement (SFBR) at the bottleneck jointly exploits spatial and frequency information to refine bottleneck features. DDR-Net achieves more effective feature representation and reconstruction for haze removal. Extensive experiments on real-world benchmarks demonstrate that our method outperforms existing dehazing approaches. It achieves competitive performance on synthetic datasets.

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