  Severe color casts, low contrast and blurriness of underwater images caused
by light absorption and scattering result in a difficult task for exploring
underwater environments. Different from most of previous underwater image
enhancement methods that compute light attenuation along object-camera path
through hazy image formation model, we propose a novel jointly wavelength
compensation and dehazing network (JWCDN) that takes into account the
wavelength attenuation along surface-object path and the scattering along
object-camera path simultaneously. By embedding a simplified underwater
formation model into generative adversarial network, we can jointly estimates
the transmission map, wavelength attenuation and background light via different
network modules, and uses the simplified underwater image formation model to
recover degraded underwater images. Especially, a multi-scale densely connected
encoder-decoder network is proposed to leverage features from multiple layers
for estimating the transmission map. To further improve the recovered image, we
use an edge preserving network module to enhance the detail of the recovered
image. Moreover, to train the proposed network, we propose a novel underwater
image synthesis method that generates underwater images with inherent optical
properties of different water types. The synthesis method can simulate the
color, contrast and blurriness appearance of real-world underwater environments
simultaneously. Extensive experiments on synthetic and real-world underwater
images demonstrate that the proposed method yields comparable or better results
on both subjective and objective assessments, compared with several
state-of-the-art methods.
