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Video Super-Resolution
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Video Super-Resolution is a computer vision task that aims to increase the resolution of a video sequence, typically from lower to higher resolutions. The goal is to generate high-resolution video frames from low-resolution input, improving the overall quality of the video.
<span style="color:grey; opacity: 0.6">( Image credit: Detail-revealing Deep Video Super-Resolution )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dachunkai/evtexture
공식 코드EvTexture: Event-driven Texture Enhancement for Video Super-Resolution
https://github.com/dmlc/xgboost
공식 코드https://github.com/esw0116/DynaVSR
공식 코드https://github.com/eugenelet/crfp
공식 코드Cross-Resolution Flow Propagation for Foveated Video Super-Resolution
https://github.com/eunjnnn/bfstvsr
공식 코드https://github.com/eyalnaor/DeepTemporalSR
공식 코드Across Scales & Across Dimensions: Temporal Super-Resolution using Deep Internal Learning
https://github.com/fcongrui/3dattgan
공식 코드https://github.com/google-research/google-research
공식 코드https://github.com/google-research/google-research/tree/master/comisr
공식 코드https://github.com/h-deep/trans-svsr
공식 코드A New Dataset and Transformer for Stereoscopic Video Super-Resolution
https://github.com/hitrainer/eavsr
공식 코드Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution
https://github.com/house-leo/cfdvsr
공식 코드Collaborative Feedback Discriminative Propagation for Video Super-Resolution
https://github.com/house-leo/ctun
공식 코드Cascaded Temporal Updating Network for Efficient Video Super-Resolution
https://github.com/hyhsimon/boosted-vsr
공식 코드Boosting Video Super Resolution with Patch-Based Temporal Redundancy Optimization
https://github.com/ianyeung/mgld-vsr
공식 코드Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolution
https://github.com/indigopurple/efenet
공식 코드EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation
https://github.com/jiangsutx/SPMC_VideoSR
공식 코드https://github.com/jingyunliang/hcflow
공식 코드Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling
https://github.com/jingyunliang/rvrt
공식 코드Recurrent Video Restoration Transformer with Guided Deformable Attention
https://github.com/jingyunliang/swinir
공식 코드https://github.com/jingyunliang/vrt
공식 코드https://github.com/jinsuyoo/SSA-VSR
공식 코드https://github.com/jiy173/MANA
공식 코드Memory-Augmented Non-Local Attention for Video Super-Resolution
https://github.com/junpan19/ETDM
공식 코드Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling
https://github.com/junpan19/RRN
공식 코드https://github.com/junpan19/RSDN
공식 코드Video Super-Resolution with Recurrent Structure-Detail Network
https://github.com/junpan19/VSR_TGA
공식 코드https://github.com/karlygzhu/rgan
공식 코드A Lightweight Recurrent Grouping Attention Network for Video Super-Resolution
https://github.com/kimsure/tcnet-for-vsr
공식 코드Temporal Consistency Learning of inter-frames for Video Super-Resolution
https://github.com/kiteretsu77/vcisr-official
공식 코드VCISR: Blind Single Image Super-Resolution with Video Compression Synthetic Data
https://github.com/kuis-ai-tekalp-research-group/perceptual-vsr
공식 코드Spatio-Temporal Perception-Distortion Trade-off in Learned Video SR
https://github.com/labshuhanggu/lrti-vsr
공식 코드https://github.com/labshuhanggu/mia-vsr
공식 코드Video Super-Resolution Transformer with Masked Inter&Intra-Frame Attention
https://github.com/linjing7/VR-Baseline
공식 코드Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration
https://github.com/littlewhitesea/STDAN
공식 코드STDAN: Deformable Attention Network for Space-Time Video Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.