An Efficient Underwater Image Enhancement Model with Extensive Beer-Lambert Law

We develop a simple yet effective model for enhancing single underwater image by mathematically extending the Beer-Lambert law. In the proposed model, we take advantage of the mean and variance of natural images to be the reference to correct color casts of underwater images. We propose an efficient strategy to recover better details of underwater images, which involves two steps: in the first step we establish a linear model associated with the mean and variance of underwater images to locate images regions containing more details, and in the second step we present a nonlinear adaptive weight scheme using this locating information to recover better details and prevent partial over-enhancement. Ultimate experiments are performed to demonstrate the effectiveness of the proposed method, and these experimental results show that our method yields better structural restoration, more naturalness color correction, and less time consumption.