Wu Qingbo, Ren Wenqi, Cao Xiaochun
IEEE Trans Image Process. 2019 Sep 30. doi: 10.1109/TIP.2019.2942504.
Most existing image dehazing methods deteriorate to different extents when processing hazy inputs with noise. The main reason is that the commonly adopted two-step strategy tends to amplify noise in the inverse operation of division by the transmission. To address this problem, we learn an interleaved Cascade of Shrinkage Fields (CSF) to reduce noise in jointly recovering the transmission map and the scene radiance from a single hazy image. Specifically, an auxiliary shrinkage field (SF) model is integrated into each cascade of the proposed scheme to reduce undesirable artifacts during the transmission estimation. Different from conventional CSF, our learned SF models have special visual patterns, which facilitate the specific task of noise reduction in haze removal. Furthermore, a numerical algorithm is proposed to efficiently update the scene radiance and the transmission map in each cascade. Extensive experiments on synthetic and real-world data demonstrate that the proposed algorithm performs favorably against state-of-the-art dehazing methods on hazy and noisy images.
大多数现有的图像去雾方法在处理带有噪声的模糊输入时都会不同程度地恶化。主要原因是常用的两步策略在除以透射率的逆运算中往往会放大噪声。为了解决这个问题，我们学习了一个交错的收缩场级联（CSF），以便在从单个模糊图像中联合恢复透射率图和场景辐射率时减少噪声。具体来说，一个辅助收缩场（SF）模型被集成到所提出方案的每个级联中，以减少透射率估计过程中不需要的伪影。与传统的CSF不同，我们学习到的SF模型具有特殊的视觉模式，这有助于在去雾中进行特定的降噪任务。此外，还提出了一种数值算法，以有效地更新每个级联中的场景辐射率和透射率图。在合成数据和真实世界数据上进行的大量实验表明，所提出的算法在模糊和有噪声的图像上优于现有的去雾方法。
