Image dehazing is an important and challenging task in image processing. Existing dehazing methods often encounter color distortion in the dehazed results. To address this issue, in this paper, we propose a novel approach named Color Enhanced Dehazing network (CED). It consists of two main branches: a dehazing branch and a color reconstruction branch. Initially, we employ Fast Fourier Transform to separate the low-frequency sub-image from the hazy image, which contains substantial color information. Alongside inputting the original hazy image into the dehazing branch, we concurrently feed the low-frequency sub-image into the color reconstruction branch. This allows us to extract and reconstruct corresponding color information to augment the dehazing process. To thoroughly fuse the information from two branches, we design a Selective Spatial-Channel adjustment fusion module (SSC) for the feature fusion across different branches. Extensive experiments on benchmark datasets well demonstrate the effectiveness and superiority of the proposed method in the image dehazing.