The phenomenon of attenuation and scattering of light propagating in water leads to such problems as color deviation and blur. These problems bring great challenges to the subsequent feature point matching task. Therefore, this paper proposes an underwater image enhancement method based on matching-oriented balance and fusion, with three self-constructed matching image-sets specialized for underwater image matching task. Specifically, we present a color balance-guided color correction module to remove color distortion issues that combine box filtering and color balance to enhance the L channel and equalize the pixel values of the a and b channels of the CIELab color model. Subsequently, we implement a percentile maximum-based contrast enhancement strategy and a multilayer transmission map estimation strategy on the color-corrected image to yield the contrast-enhanced foreground and dehazed background sub-images. Finally, we employ a principal component fusion module to reconstruct a high-visibility underwater image by integrating the advantages of the foreground contrast-enhanced sub-image and the background dehazed sub-image. Numerous experiments on three self-constructed matching image-sets have found that the proposed method has the largest number of feature point pairs. This demonstrates that the proposed method has good color correction ability for hazy and low-light underwater images, where the processed images have more details suitable for feature point matching.