  Underwater images are typically characterized by color cast, haze, blurring,
and uneven illumination due to the selective absorption and scattering when
light propagates through the water, which limits their practical applications.
Underwater image enhancement and restoration (UIER) is one crucial mode to
improve the visual quality of underwater images. However, most existing UIER
methods concentrate on enhancing contrast and dehazing, rarely pay attention to
the local illumination differences within the image caused by illumination
variations, thus introducing some undesirable artifacts and unnatural color. To
address this issue, an effective variational framework is proposed based on an
extended underwater image formation model (UIFM). Technically, dual high-order
regularizations are successfully integrated into the variational model to
acquire smoothed local ambient illuminance and structure-revealed reflectance
in a unified manner. In our proposed framework, the weight factors-based color
compensation is combined with the color balance to compensate for the
attenuated color channels and remove the color cast. In particular, the local
ambient illuminance with strong robustness is acquired by performing the local
patch brightest pixel estimation and an improved gamma correction.
Additionally, we design an iterative optimization algorithm relying on the
alternating direction method of multipliers (ADMM) to accelerate the solution
of the proposed variational model. Considerable experiments on three real-world
underwater image datasets demonstrate that the proposed method outperforms
several state-of-the-art methods with regard to visual quality and quantitative
assessments. Moreover, the proposed method can also be extended to outdoor
image dehazing, low-light image enhancement, and some high-level vision tasks.
The code is available at https://github.com/Hou-Guojia/UDHTV.
