https://github.com/yuehan717/pdd
공식 코드Pairwise Distance Distillation for Unsupervised Real-World Image Super-Resolution
Papers with Code 아카이브
Image Super-Resolution
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Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yuehan717/realviformer
공식 코드RealViformer: Investigating Attention for Real-World Video Super-Resolution
https://github.com/yulunzhang/RCAN
공식 코드Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/yulunzhang/RDN
공식 코드https://github.com/yumingj/C2-Matching
공식 코드Reference-based Image and Video Super-Resolution via C2-Matching
https://github.com/yun224/dukd
공식 코드Data Upcycling Knowledge Distillation for Image Super-Resolution
https://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/zbzhzhy/PZRes-Net
공식 코드Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning
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/zhaohengyuan1/PAN
공식 코드https://github.com/zhaominyiz/c3-stisr
공식 코드C3-STISR: Scene Text Image Super-resolution with Triple Clues
https://github.com/zhengchen1999/bi-diffsr
공식 코드https://github.com/zhengchen1999/dat
공식 코드https://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/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/zhjy2016/splut
공식 코드Learning Series-Parallel Lookup Tables for Efficient Image Super-Resolution
https://github.com/zhoumumu/vapsr
공식 코드Efficient Image Super-Resolution using Vast-Receptive-Field Attention
https://github.com/zihuiwu/PnP-DM-public
공식 코드Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors
https://github.com/zijianhu/traffic_density_estimation
공식 코드Turning Traffic Monitoring Cameras into Intelligent Sensors for Traffic Density Estimation
https://github.com/zillur-av/wrong-way-vehicle-detection
공식 코드A Real-Time Wrong-Way Vehicle Detection Based on YOLO and Centroid Tracking
https://github.com/zirui0625/difiisr
공식 코드DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-Resolution
https://github.com/zj-binxia/enlca
공식 코드Efficient Non-Local Contrastive Attention for Image Super-Resolution
https://github.com/zj-binxia/kdsr
공식 코드Knowledge Distillation based Degradation Estimation for Blind Super-Resolution
https://github.com/zmzhang1998/Real-RawVSR
공식 코드Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset
https://github.com/zononhzy/lamnet
공식 코드Unifying Dimensions: A Linear Adaptive Approach to Lightweight Image Super-Resolution
https://github.com/zsyOAOA/BSRDM
공식 코드Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel
https://github.com/zsyOAOA/VIRNet
공식 코드https://github.com/zsyoaoa/invsr
공식 코드Arbitrary-steps Image Super-resolution via Diffusion Inversion
https://github.com/zsyoaoa/resshift
공식 코드ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting
https://github.com/zth667/Diverse-Image-Synthesis-from-Semantic-Layout
공식 코드Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.