Video super-resolution (VSR) is a typical ill-posed problem aiming to increase spatial resolution of a video. To explore interframe information between adjacent frames, optical flow and deformable convolution are commonly used for temporal dependency modeling. However, they lack the ability to use nonlocal spatial information. We propose the dual-branch feature extraction network for VSR. Our method consists of feature extraction, feature fusion and refinement, and frame reconstruction. Specifically, the feature extraction is designed to explore both local and nonlocal information of input frames. For the local feature extraction branch, we use deformable convolutions to eliminate motion between frames. For the nonlocal feature extraction branch, we employ a nonlocal module to gather relevant features. We concatenate these extracted features on channel dimension for progressive feature fusion and refinement. The frame reconstruction part is responsible for increasing spatial resolution and reconstruction. Experimental results demonstrate the effectiveness of the proposed method by extensive quantitative and qualitative evaluations in public benchmark datasets.