Abstract: Convolutional neural networks (CNNs) have made great breakthrough in the field of image super-resolution (SR). However, most current methods are usually to improve their performance by simply increasing the depth of their network. Although this strategy can get promising results, it is inefficient in many real-world scenarios because of the high computational cost. In this paper, we propose an efficient group feature fusion residual network (GFFRN) for image super-resolution. In detail, we design a novel group feature fusion residual block (GFFRB) to group and fuse the features of the intermediate layer. In this way, GFFRB can enjoy the merits of the lightweight of the group convolution and the high-efficiency of the skip connections, thus achieving better performance compared with most current residual blocks. Experiments on the benchmark test sets show that our models are more efficient than most of the state-of-the-art methods.