The work aims to improve the performance of image inpainting. IECNN, an universal image inpainting method based on Implicit Knowledge Transfer and Explicit Mask Guide, was proposed. The proposed universal image inpainting task was clearly divided into two stages of degraded region localization and region guided restoration. Firstly, the degradation localization knowledge inherent in mask prediction network was used and trained to detect the severely degraded region. Then, an adaptive attentional knowledge distillation method was proposed to transfer the degraded region knowledge implicitly into the restoration network without any extra inference cost. Then, two mask guided modules were proposed to extend the global receptive field and focus on the degraded region, so as to explicitly restore the image. In the ablation experiment, the effectiveness of each component was visually demonstrated by visual feature maps and pairwise relationship maps. In order to prove the universality of the proposed method, two indexes, peak signal to noise ratio and structural similarity, were quantitatively compared with other reference methods in four spatially varied image restoration tasks. Then, qualitative comparison was made on visual effect. The results show that implicit knowledge transfer and explicit mask guide are effective for image inpainting.