https://github.com/zhoumumu/vapsr
공식 코드Efficient Image Super-Resolution using Vast-Receptive-Field Attention
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/zhwzhong/AHMF
공식 코드High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion
https://github.com/zhwzhong/Guided-Depth-Map-Super-resolution-A-Survey
공식 코드https://github.com/zhwzhong/dagf
공식 코드https://github.com/zifancui/kedusr
공식 코드KeDuSR: Real-World Dual-Lens Super-Resolution via Kernel-Free Matching
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/amsa
공식 코드https://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/zj-binxia/mrda
공식 코드Meta-Learning based Degradation Representation for Blind Super-Resolution
https://github.com/zj-binxia/ssl
공식 코드Structured Sparsity Learning for Efficient Video Super-Resolution
https://github.com/zju-jiangqi/pcie-part
공식 코드Minimalist and High-Quality Panoramic Imaging with PSF-aware Transformers
https://github.com/zmzhang1998/Real-RawVSR
공식 코드Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset
https://github.com/zongyi-li/fourier_neural_operator
공식 코드Fourier Neural Operator for Parametric Partial Differential Equations
https://github.com/zononhzy/lamnet
공식 코드Unifying Dimensions: A Linear Adaptive Approach to Lightweight Image Super-Resolution
https://github.com/zpc456/akmd-net
공식 코드Jointly RS Image Deblurring and Super-Resolution with Adjustable-Kernel and Multi-Domain Attention
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
https://github.com/zudi-lin/rcan-it
공식 코드Revisiting RCAN: Improved Training for Image Super-Resolution
https://github.com/zwang78/moediff-sr
공식 코드MoEDiff-SR: Mixture of Experts-Guided Diffusion Model for Region-Adaptive MRI Super-Resolution
https://github.com/zwang78/sr
공식 코드Distillation-Driven Diffusion Model for Multi-Scale MRI Super-Resolution: Make 1.5T MRI Great Again
https://github.com/zxlation/FC2N
공식 코드FC$^2$N: Fully Channel-Concatenated Network for Single Image Super-Resolution
https://github.com/zysxmu/ddtb
공식 코드Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks
https://github.com/zysxmu/dfsq
공식 코드Distribution-Flexible Subset Quantization for Post-Quantizing Super-Resolution Networks
https://github.com/zzh-tech/bit
공식 코드Blur Interpolation Transformer for Real-World Motion from Blur
https://gitlab.com/Ryukijano/ESRGAN_AND_YOLOV7
공식 코드https://gitlab.com/tarasiewicztomasz/deepsent
공식 코드Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 images
https://gitlab.lms.tf.fau.de/LMS/lfcr_public
공식 코드https://github.com/04RR/SOTA-Vision
Single Image Super-Resolution via a Holistic Attention Network
https://github.com/07Agarg/Image-Resolution-Enhancement-SRCNN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.