DFR-Net: Density Feature Refinement Network for Image Dehazing Utilizing Haze Density Difference

In the image dehazing task, the haze density is a key feature that affects the performance of dehazing methods. The haze density difference, which has rarely been utilized in previous methods, can guide networks to perceive different global densities and focus on local areas with high density or that are difficult to dehaze. In this paper, we propose a density-aware dehazing method named the Density Feature Refinement Network (DFR-Net), which extracts haze density features from density differences and leverages density differences to refine density features. In DFR-Net, we first generate a proposal image that has a lower overall density than the hazy input, resulting in global density differences. Additionally, the dehazing residual of the proposal image reflects the level of dehazing performance and provides local density differences that indicate localized hard dehazing or high-density areas. Subsequently, we introduce a Global Branch (GB) and a Local Branch (LB) to achieve density awareness. In GB, we use Siamese networks for feature extraction of hazy inputs and proposal images, and we propose a Global Density Feature Refinement (GDFR) module that can refine features by pushing features with different global densities further away. In LB, we explore local density features from the dehazing residuals between hazy inputs and proposal images and introduce an Intermediate Dehazing Residual Feedforward (IDRF) module to update local features and pull them close to clear image features. Sufficient experiments demonstrate that the proposed method outperforms state-of-the-art methods on various datasets.