Standard convolution applied to image inpainting would lead to color discrepancy and blurriness for treating valid and invalid/hole regions without difference, which was partially amended by partial convolution (PConv). In PConv, a binary/hard mask was maintained as an indicator of valid and invalid pixels, where valid pixels and invalid pixels were treated differently. However, it can not describe validity degree of an impaired pixel. In addition, mask and image paths were separated, without sharing convolution kernel and exchanging information mutually, reducing data utilization efficiency. In this paper, a mask-guided convolution (MagConv) is proposed for image inpainting. In MagConv, mask and image paths share a convolution kernel to interact with each other and form a joint optimization scheme. In addition, a learnable piecewise activation function is raised to replace the reciprocal function of PConv, providing more flexible and adaptable compensation to convolution contaminated by invalid pixels. It also results in a soft mask of floating-point coefficients from 0 to 1 capable of indicating the validity degree of each pixel. Last but not least, MagConv splits the convolution kernel into positive and negative weights so that they can evaluate the validity of each pixel faithfully. Qualitative and quantitative experiments on the CelebA, Paris StreetView and Places2 datasets demonstrate that our method achieves favorable visual quality against state-of-the-art approaches.