  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.
