The existing image deblurring methods typically directly use spatial or frequency domain information to restore clear images, ignoring the complementarity of spatial and frequency domain information. Utilizing the spatial domain information of images can effectively restore object structures while utilizing the frequency domain information of images can effectively restore texture details. This study proposes a simple and effective image deblurring framework that can fully utilize both the spatial and frequency domain information of images to produce high-quality and clear images. Firstly, two independent networks with the same structure are employed to learn the mapping relationship from the blurred images to the clear images in the spatial and frequency domain, respectively. Then a separate fusion network is adopted to further elevate the quality of clear images by fully integrating image information from both spatial and frequency domains. The three networks can be linked to form an end-to-end trainable large network, where they interact with each other to obtain high-quality images by joint optimization. The proposed method surpasses 9 state-of-the-art image deblurring methods in terms of peak signal-to-noise ratio, structural similarity index metric, and mean absolute error on the public image deblurring datasets including GoPro, Kohler, and RWBI. The effectiveness of the proposed image deblurring method which integrates both spatial and frequency domain information is verified by a large number of experiments.