In image dehazing task, haze density is a key feature and affects the performance of dehazing methods. However, some of the existing methods lack a comparative image to measure densities, and others create intermediate results but lack the exploitation of their density differences, which can facilitate perception of density. To address these deficiencies, we propose a density-aware dehazing method named Density Feature Refinement Network (DFR-Net) that 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 lower overall density than the hazy input, bringing 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 closer to clear image features. Sufficient experiments demonstrate that the proposed method achieves results beyond the state-of-the-art methods on various datasets.

翻译：在图像去雾任务中，雾霾密度是影响去雾方法性能的关键特征。然而，现有方法中部分缺乏用于密度测量的对比图像，另一部分虽能生成中间结果却未充分利用其密度差异——而该差异有助于感知密度。针对这些不足，本文提出一种密度感知去雾方法——密度特征精炼网络（DFR-Net），通过从密度差异中提取雾霾密度特征，并利用密度差异精炼密度特征。DFR-Net首先生成一个整体密度低于雾霾输入图像的候选图像，从而引入全局密度差异。此外，候选图像的去雾残差反映了去雾性能水平，并提供指示局部去雾难点或高密度区域的局部密度差异。随后，我们引入全局分支（GB）和局部分支（LB）实现密度感知。在全局分支中，我们使用孪生网络提取雾霾输入图像与候选图像的特征，并提出全局密度特征精炼（GDFR）模块，通过拉远具有不同全局密度的特征实现特征精炼。在局部分支中，我们从雾霾输入图像与候选图像的去雾残差中探索局部密度特征，并引入中间去雾残差前馈（IDRF）模块更新局部特征，使其向清晰图像特征靠近。充分的实验表明，所提方法在多个数据集上的性能均超越当前最先进方法。