In commercial digital cameras， due to the limitation of Complementary Metal Oxide Semiconductor （CMOS） sensors， there is only one color channel information for each pixel in the sampled image. Therefore， the Color image DeMosaicking （CDM） algorithm is required to restore the full-color images. However， most of the existing Convolutional Neural Network （CNN）-based CDM algorithms cannot achieve satisfactory performance with relatively low computational complexity and small network parameter number. To solve this problem， a CDM network based on Inter-channel Correlation and Enhanced Information Distillation （ICEID） was proposed. Firstly， to fully utilize the inter-channel correlation of the color image， an inter-channel guided reconstruction structure was designed to obtain the initial CDM result. Secondly， an Enhanced Information Distillation Module （EIDM）， which can effectively extract and refine features from image with relatively small parameter number， was presented to enhance the reconstructed full-color image in high efficiency. Experimental results demonstrate that compared with many state-of-the-art CDM methods， the proposed algorithm achieves significant improvement in both objective quality and subjective quality， and has relatively low computational complexity and small network parameter number.