Single Underwater Image Restoration Using Adaptive Attenuation-Curve Prior

Underwater imaging is an important topic in maritime research. Due to the existence of dust-like particles in water medium, underwater images are vulnerable to the effect of low contrast and color cast. In this paper, we propose a novel underwater image restoration method based on a non-local prior, namely, adaptive attenuation-curve prior. This prior relies on the statistical distribution of pixel values. That is, all pixel values of a clear image can be partitioned into several hundred distinct clusters in RGB space, and the pixel values in each cluster will be distributed on a curve with a power function form after attenuated by water in varying degrees. Specifically, we can estimate the transmission for each pixel according to its distribution on the curves. Then, we estimate the attenuation factor to compensate for the transmission. To prevent over saturation and reduce the noise of the recovered images, we propose the saturation constraints to adjust the transmission of the three color channels. Qualitative and quantitative results demonstrate that our proposed method can achieve better performance, compared with the state-of-the-art approaches. Moreover, our proposed method can be further extended to restore other kinds of degraded images, such as hazy images.

I. Introduction

Underwater vision is one of the most fundamental parts in marine scientific research and ocean engineering, such as underwater imaging technology that helps subsea exploration to study marine biology and inspect geological environment. In addition, autonomous underwater vehicle (AUV) relies on vision methods to control itself in complicated condition. However, light attenuation poses a threat to high-quality underwater images/videos, which leads to haze-like surroundings for the underwater imaging system, and hinders most computer vision applications in the maritime environment [1].