The MRI Image Interpolation Algorithm Based on Bidirectional Optical Flow with Cyclic Consistency Constraint

Optical flow estimation is widely used in fields such as video frame interpolation, object tracking, and scene understanding. Although existing deep learning models have significantly improved the accuracy of optical flow estimation, they are often hindered by high model complexity and a lack of effective cyclic consistency constraints for the generated optical flows. To address this issue, this paper proposes a lightweight optical flow estimation model and uses cyclic consistency constraints to improve the quality of both forward and backward optical flow generation. The main contributions of this paper are: proposing a lightweight optical flow estimation model based on U-Net and introducing cyclic consistency constraints to enhance both the accuracy and physical consistency of optical flow generation. Experimental results on the knee joint dataset show that the improved bidirectional optical flow estimation module reduces the number of parameters by approximately 37.47%, while achieving higher PSNR and SSIM than the original module.