Color cast is a crucial problem for color image processing. White balance has been widely used to eliminate color cast to improve the image's quality. Most of white balance implementations are based on color constancy hypothesis. A well-known color constancy hypothesis is given in [1], unifying White Patch [2], Grey World [3], Shades of Grey [4], and Grey Edge [1] assumptions in one expression. However, this general hypothesis works on underwater images not as reliable as on common images. In the color constancy hypothesis for common scenes, the ambient light source is spatial constant. But in underwater scenes, the light suffers from serious attenuation, especially in the red part of the visible spectrum. This attenuation causes spatial variance of the ambient light source, which lets classic color constancy hypothesis fail. In this paper, we propose a novel low-level image feature-based color constancy hypothesis for underwater scenes. Based on this hypothesis, we propose an algorithm, using a distance map to estimate multiple gain factors to remove the color cast.