We propose reparameterized refocusing convolution (RefConv) as a replacement for regular convolutional layers, which is a plug-and-play module to improve the performance without any inference costs. Specifically, given a pretrained model, RefConv applies a trainable Refocusing Transformation to the basis kernels inherited from the pretrained model to establish connections among the parameters. For example, a depthwise RefConv can relate the parameters of a specific channel of convolution kernel to the parameters of the other kernel, i.e., make them refocus on the other parts of the model they have never attended to, rather than focus on the input features only. From another perspective, RefConv augments the priors of existing model structures by utilizing the representations encoded in the pretrained parameters as the priors and refocusing on them to learn novel representations, thus further enhancing the representational capacity of the pretrained model. The experimental results validated that RefConv can improve multiple convolutional neural network (CNN)-based models by a clear margin on image classification (up to 1.47% higher top-1 accuracy on ImageNet), object detection, semantic segmentation, and adversarial attacks without introducing any extra inference costs or altering the original model structure. Further studies demonstrated that RefConv can strengthen the spatial skeletons of kernels, reduce the redundancy of channels, and smooth the loss landscape, which explains its effectiveness.