https://github.com/linjing7/VR-Baseline
공식 코드Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration
Papers with Code 아카이브
Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.
<span style="color:grey; opacity: 0.6">( Credit: MemNet )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/liouxy/tfnet_pytorch
공식 코드Remote Sensing Image Fusion Based on Two-stream Fusion Network
https://github.com/lipang/hirdiff
공식 코드HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models
https://github.com/lipang/hsigene
공식 코드HSIGene: A Foundation Model For Hyperspectral Image Generation
https://github.com/liqilei/GMFN
공식 코드https://github.com/lironui/geosr
공식 코드LSwinSR: UAV Imagery Super-Resolution based on Linear Swin Transformer
https://github.com/littlebeen/asddpm-adaptive-semantic-enhanced-ddpm
공식 코드https://github.com/littlepure2333/ape
공식 코드Adaptive Patch Exiting for Scalable Single Image Super-Resolution
https://github.com/littlepure2333/samplingaug
공식 코드SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution
https://github.com/littlewhitesea/STDAN
공식 코드STDAN: Deformable Attention Network for Space-Time Video Super-Resolution
https://github.com/littleyaang/hq-50k
공식 코드HQ-50K: A Large-scale, High-quality Dataset for Image Restoration
https://github.com/liturout/psld
공식 코드Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models
https://github.com/liu-zihang/samsr
공식 코드Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution
https://github.com/liuguandu/rc-lut
공식 코드Reconstructed Convolution Module Based Look-Up Tables for Efficient Image Super-Resolution
https://github.com/liumency/SRCDNet
공식 코드https://github.com/liuofficial/sdp
공식 코드A Spectral Diffusion Prior for Hyperspectral Image Super-Resolution
https://github.com/lixinustc/awesome-diffusion-model-for-image-processing
공식 코드Diffusion Models for Image Restoration and Enhancement -- A Comprehensive Survey
https://github.com/lixinustc/kvqe-challenge-cvpr-ntire2025
공식 코드NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results
https://github.com/liyuantsao/BFSR
공식 코드Boosting Flow-based Generative Super-Resolution Models via Learned Prior
https://github.com/liyuantsao/HoliSDiP
공식 코드HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion Prior
https://github.com/llmpass/RSTT
공식 코드RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-Resolution
https://github.com/lms-07/lgteun
공식 코드Local-Global Transformer Enhanced Unfolding Network for Pan-sharpening
https://github.com/lonelyhope/dada
공식 코드Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution
https://github.com/lose4578/SAM-DiffSR
공식 코드SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolution
https://github.com/lovepiano/SMFN
공식 코드A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-Resolution
https://github.com/lovepiano/SMFN_For_360VSR
공식 코드A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-Resolution
https://github.com/lpcccc-cv/mc-cdic
공식 코드https://github.com/lpj-github-io/MWCNNv2
공식 코드https://github.com/lpj0/MWCNN
공식 코드https://github.com/lpj0/bde
공식 코드https://github.com/lqm26/bmcnet-esr
공식 코드Bilateral Event Mining and Complementary for Event Stream Super-Resolution
https://github.com/lqm26/rmfnet
공식 코드Efficient Event Stream Super-Resolution with Recursive Multi-Branch Fusion
https://github.com/ltu-machine-learning/analysis-of-deep-perceptual-loss-networks
공식 코드https://github.com/luciennnnnnn/dualformer
공식 코드On the Effectiveness of Spectral Discriminators for Perceptual Quality Improvement
https://github.com/luigisigillo/shipinsight
공식 코드Ship in Sight: Diffusion Models for Ship-Image Super Resolution
https://github.com/luissen/esrt
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.