RefVSR++: Exploiting Reference Inputs for Reference-based Video Super-resolution
This work addresses video quality enhancement for smartphone users with multi-camera setups, representing an incremental improvement over existing reference-based approaches.
The paper tackles video super-resolution by leveraging higher-resolution reference videos from multi-camera systems to enhance lower-resolution videos, achieving over 1dB improvement in PSNR compared to previous methods.
Smartphones with multi-camera systems, featuring cameras with varying field-of-views (FoVs), are increasingly common. This variation in FoVs results in content differences across videos, paving the way for an innovative approach to video super-resolution (VSR). This method enhances the VSR performance of lower resolution (LR) videos by leveraging higher resolution reference (Ref) videos. Previous works, which operate on this principle, generally expand on traditional VSR models by combining LR and Ref inputs over time into a unified stream. However, we can expect that better results are obtained by independently aggregating these Ref image sequences temporally. Therefore, we introduce an improved method, RefVSR++, which performs the parallel aggregation of LR and Ref images in the temporal direction, aiming to optimize the use of the available data. RefVSR++ also incorporates improved mechanisms for aligning image features over time, crucial for effective VSR. Our experiments demonstrate that RefVSR++ outperforms previous works by over 1dB in PSNR, setting a new benchmark in the field.