https://github.com/yulunzhang/RCAN
공식 코드Image Super-Resolution Using Very Deep Residual Channel Attention Networks
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/yulunzhang/RDN
공식 코드https://github.com/yulunzhang/RNAN
공식 코드https://github.com/yumingj/C2-Matching
공식 코드https://github.com/yun224/dukd
공식 코드Data Upcycling Knowledge Distillation for Image Super-Resolution
https://github.com/yunfanLu/INR-Event-VSR
공식 코드Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution
https://github.com/yunfanlu/hr-inr
공식 코드HR-INR: Continuous Space-Time Video Super-Resolution via Event Camera
https://github.com/yunyagaotree/sam-adapter-for-refugee-dwelling-extraction
공식 코드https://github.com/yuqing-liu-dut/ISRN
공식 코드https://github.com/yuqing-liu-dut/nriqa_sr
공식 코드Textural-Perceptual Joint Learning for No-Reference Super-Resolution Image Quality Assessment
https://github.com/yylgoodlucky/hdtr
공식 코드https://github.com/zbzhzhy/PZRes-Net
공식 코드Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning
https://github.com/zcsrenlongz/rbsr
공식 코드RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution
https://github.com/zeqiang-lai/dphsir
공식 코드Deep Plug-and-Play Prior for Hyperspectral Image Restoration
https://github.com/zerono000/neurop-diff
공식 코드NeurOp-Diff:Continuous Remote Sensing Image Super-Resolution via Neural Operator Diffusion
https://github.com/zeyuxiao1997/EDPN
공식 코드EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration
https://github.com/zeyuxiao1997/reallfsr
공식 코드https://github.com/zhaodongsun/pnp_dip
공식 코드Solving Inverse Problems with Hybrid Deep Image Priors: the challenge of preventing overfitting
https://github.com/zhaohengyuan1/PAN
공식 코드https://github.com/zhaominyiz/c3-stisr
공식 코드C3-STISR: Scene Text Image Super-resolution with Triple Clues
https://github.com/zhaoxiaoyu1995/recfno
공식 코드https://github.com/zhaoyuzhi/svcnet
공식 코드SVCNet: Scribble-based Video Colorization Network with Temporal Aggregation
https://github.com/zhaozixiang1228/gdsr-dctnet
공식 코드Discrete Cosine Transform Network for Guided Depth Map Super-Resolution
https://github.com/zhengchen1999/bi-diffsr
공식 코드https://github.com/zhengchen1999/dat
공식 코드https://github.com/zhengchen1999/dove
공식 코드DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-Resolution
https://github.com/zhengchen1999/ntire2024_imagesr_x4
공식 코드NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
https://github.com/zhengchen1999/ntire2025_imagesr_x4
공식 코드NTIRE 2025 Challenge on Image Super-Resolution ($\times$4): Methods and Results
https://github.com/zhengchen1999/promptsr
공식 코드https://github.com/zhengchen1999/rgt
공식 코드Recursive Generalization Transformer for Image Super-Resolution
https://github.com/zhengdharia/LUD-VAE
공식 코드Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach
https://github.com/zhengdharia/SeNM-VAE
공식 코드SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder
https://github.com/zhengyuliang24/basiclfsr
공식 코드NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results
https://github.com/zhengyuliang24/epit
공식 코드Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution
https://github.com/zhiyuan-yang/Edge-Guided-Cross-Scale-MRI-Super-resolution
공식 코드Edge-guided and Cross-scale Feature Fusion Network for Efficient Multi-contrast MRI Super-Resolution
https://github.com/zhjy2016/splut
공식 코드Learning Series-Parallel Lookup Tables for Efficient Image Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.