Non-linear Motion Estimation Network for Frame Interpolation in Coronary Angiographic Sequences

Video frame interpolation (VFI) is significant for generating a high frame rate coronary sequence without additional radiation exposure. Due to the coronary reciprocating pattern alternating between systolic and diastolic phases, the linear assumption-based existing methods fail to capture the complex motion especially during the transitions between the two phases. Different from the linear methods, a Non-linear Motion Estimation Network (NLME-Net) is proposed to effectively capture the periodic reciprocating motion pattern by accurately estimating both bidirectional flows and long-distance motion. Specifically, the specialized motion estimation decoder is guided not only by target frame reconstruction loss but also by direct supervision through a self-supervised flow loss. This enhanced modeling of reciprocating motion enables accurate intermediate flow estimation in scenarios involving variable directional movement, thereby improving the accuracy and robustness of frame interpolation. Additionally, the interpolation decoder fully exploits the inherent mutual dependency between intermediate flow and target frame features to refine the final interpolation result. According to the experiment results of the proposed and twelve state-of-the-art methods using the coronary dataset with 6486 groups of angiographic images from 399 sequences, the proposed method improves the PSNR score by an average 0.59dB.