Image noise usually causes depth-dependent visual artifacts in single image dehazing. Most existing dehazing methods exploit a two-step strategy in the restoration, which inevitably leads to inaccurate transmission maps and low-quality scene radiance for noisy and hazy inputs. To address these problems, we present a novel variational model for joint recovery of the transmission map and the scene radiance from a single image. In the model, we propose a transmission-aware non-local regularization to avoid noise amplification by adaptively suppressing noise and preserving fine details in the recovered image. Meanwhile, to improve the accuracy of transmission estimation, we introduce a semantic-guided regularization to smooth out the transmission map while keeping depth inconsistency at the boundaries of different objects. Furthermore, we design an alternating scheme to jointly optimize the transmission map and the scene radiance as well as the segmentation map. Extensive experiments on synthetic and real-world data demonstrate that the proposed algorithm performs favorably against state-of-the-art dehazing methods on noisy and hazy images.

中文翻译：

---

用于消除单个图像中的雾霾和噪声的准确传输估计。


图像噪声通常会在单图像去雾中导致深度相关的视觉伪影。大多数现有的去雾方法在恢复过程中采用两步策略，这不可避免地导致传输图不准确以及噪声和模糊输入的低质量场景辐射。为了解决这些问题，我们提出了一种新颖的变分模型，用于从单个图像中联合恢复传输图和场景辐射率。在该模型中，我们提出了一种传输感知的非局部正则化，通过自适应抑制噪声并保留恢复图像中的精细细节来避免噪声放大。同时，为了提高传输估计的准确性，我们引入了语义引导的正则化来平滑传输图，同时保持不同对象边界处的深度不一致。此外，我们设计了一种交替方案来联合优化传输图和场景辐射度以及分割图。对合成数据和真实世界数据的大量实验表明，所提出的算法在噪声和模糊图像上的性能优于最先进的去雾方法。