Frame Interpolation Using Phase And Amplitude Feature Pyramids

This paper presents a compact neural network for video frame interpolation using phase and amplitude feature pyramids. We design a set of one-dimensional separable complex Gabor filters to extract phase and amplitude feature pyramids for each input image, which is efficient and effective for motion representation. The pyramids are fused and fed into a decoder network to estimate bi-directional optical flow. The interpolated frame is refined by a context-aware synthesis module. We train our model on quintets of frames using motion linear regularization. The proposed network contains much fewer parameters than state-of-the-art approaches. The experiments show that our method outperforms the competing methods. Moreover, our method achieves marked visual improvement in the challenging scenario with lighting changes.