  Underwater images are inevitably affected by color distortion and reduced
contrast. Traditional statistic-based methods such as white balance and
histogram stretching attempted to adjust the imbalance of color channels and
narrow distribution of intensities a priori thus with limited performance.
Recently, deep-learning-based methods have achieved encouraging results.
However, the involved complicate architecture and high computational costs may
hinder their deployment in practical constrained platforms. Inspired by above
works, we propose a statistically guided lightweight underwater image
enhancement network (USLN). Concretely, we first develop a dual-statistic white
balance module which can learn to use both average and maximum of images to
compensate the color distortion for each specific pixel. Then this is followed
by a multi-color space stretch module to adjust the histogram distribution in
RGB, HSI, and Lab color spaces adaptively. Extensive experiments show that,
with the guidance of statistics, USLN significantly reduces the required
network capacity (over98%) and achieves state-of-the-art performance. The code
and relevant resources are available at https://github.com/deepxzy/USLN.
