Enhancing Deformable Convolution based Video Frame Interpolation with Coarse-to-fine 3D CNN
This work addresses video frame interpolation for video processing applications, representing an incremental improvement over existing methods.
The paper tackled video frame interpolation by proposing a deformable convolution-based method with a coarse-to-fine 3D CNN for multi-flow prediction, achieving superior performance with PSNR gains up to 0.19dB over state-of-the-art methods.
This paper presents a new deformable convolution-based video frame interpolation (VFI) method, using a coarse to fine 3D CNN to enhance the multi-flow prediction. This model first extracts spatio-temporal features at multiple scales using a 3D CNN, and estimates multi-flows using these features in a coarse-to-fine manner. The estimated multi-flows are then used to warp the original input frames as well as context maps, and the warped results are fused by a synthesis network to produce the final output. This VFI approach has been fully evaluated against 12 state-of-the-art VFI methods on three commonly used test databases. The results evidently show the effectiveness of the proposed method, which offers superior interpolation performance over other state of the art algorithms, with PSNR gains up to 0.19dB.