Video Super Resolution On Vid4 4X Upscaling 1

Metrics

PSNR

SSIM

Results

Performance results of various models on this benchmark

			Paper Title
RVRT	29.54	0.8810	Recurrent Video Restoration Transformer with Guided Deformable Attention
VRT	29.42	0.8795	VRT: A Video Restoration Transformer
BasicVSR++	29.04	0.8753	BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment
FTVSR	28.7	0.869	Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-Resolution
GOVSR	28.41	0.8724	Omniscient Video Super-Resolution
TTVSR	28.40	0.8643	Learning Trajectory-Aware Transformer for Video Super-Resolution
IconVSR	28.04	0.8570	BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
BasicVSR	27.96	0.8553	BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
RSDN	27.92	0.8505	Video Super-Resolution with Recurrent Structure-Detail Network
EDVR	27.85	0.8503	EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
RRN	27.69	0.8488	Revisiting Temporal Modeling for Video Super-resolution
TGA	27.63	0.8423	Video Super-resolution with Temporal Group Attention
RLSP	27.48	0.8388	Efficient Video Super-Resolution through Recurrent Latent Space Propagation
DUF	27.38	0.8329	Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation
RBPN	27.17	0.8205	Recurrent Back-Projection Network for Video Super-Resolution
PFNL	27.16	0.8355	Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal Correlations
FRVSR	26.69	0.8103	Frame-Recurrent Video Super-Resolution
TOFlow	25.85	0.7659	Video Enhancement with Task-Oriented Flow

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Video Super Resolution On Vid4 4X Upscaling 1 | SOTA | HyperAI