Images with haze of different varieties often pose a significant challenge to dehazing. Therefore, guidance by estimates of haze parameters related to the variety would be beneficial, and their progressive update jointly with haze reduction will allow effective dehazing. To this end, we propose a multi-network dehazing framework containing novel interdependent dehazing and haze parameter updater networks that operate in a progressive manner. The haze parameters, transmission map and atmospheric light, are first estimated using dedicated convolutional networks that allow color-cast handling. The estimated parameters are then used to guide our dehazing module, where the estimates are progressively updated by novel convolutional networks. The updating takes place jointly with progressive dehazing using a network that invokes inter-step dependencies. The joint progressive updating and dehazing gradually modify the haze parameter values toward achieving effective dehazing. Through different studies, our dehazing framework is shown to be more effective than image-to-image mapping and predefined haze formation model based dehazing. The framework is also found capable of handling a wide variety of hazy conditions wtih different types and amounts of haze and color casts. Our dehazing framework is qualitatively and quantitatively found to outperform the state-of-the-art on synthetic and real-world hazy images of multiple datasets with varied haze conditions.

翻译：不同种类的雾霾图像对去雾提出了显著挑战。因此，利用与雾霾种类相关的参数估计进行引导具有优势，而参数估计与去雾的联合渐进式更新则能实现有效的去雾。为此，我们提出了一种包含新型相互依赖去雾网络和雾霾参数更新网络的多网络去雾框架，该框架以渐进式方式运行。首先，通过专用卷积网络估计雾霾参数（透射图和大气光），并支持色偏处理。随后，利用这些估计参数指导去雾模块，在该模块中，由新型卷积网络对参数估计值进行渐进更新。该更新过程通过利用跨步骤依赖关系的网络与渐进式去雾联合进行。这种联合渐进式更新与去雾逐渐调整雾霾参数值，从而实现高效去雾。通过多项研究，我们提出的去雾框架被证明比基于图像到图像映射和基于预设雾霾形成模型的去雾方法更有效。该框架还能处理包含不同雾霾类型、浓度及色偏的多种雾霾条件。在多个包含不同雾霾条件的合成和真实雾霾图像数据集上的定性和定量结果表明，我们的去雾框架优于现有最优方法。