https://github.com/mjq11302010044/tatt
공식 코드A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution
Papers with Code 아카이브
Image Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 평가표
- 69
- 보존된 평가 기록
- 748
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/mlvlab/davi
공식 코드Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems
https://github.com/mmafrasiabi/comsr
공식 코드Combination of Single and Multi-frame Image Super-resolution: An Analytical Perspective
https://github.com/mmmings/granular-dq
공식 코드https://github.com/moonsofang/ntire-2025-srlab
공식 코드Enhanced Semantic Extraction and Guidance for UGC Image Super Resolution
https://github.com/mr-bamboo/mfg-hmoe
공식 코드Heterogeneous Mixture of Experts for Remote Sensing Image Super-Resolution
https://github.com/msu-video-group/erqa
공식 코드ERQA: Edge-Restoration Quality Assessment for Video Super-Resolution
https://github.com/mv-lab/AISP
공식 코드https://github.com/mv-lab/swin2sr
공식 코드Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration
https://github.com/naagar/art_restoration_dm
공식 코드Adaptation of the super resolution SOTA for Art Restoration in camera capture images
https://github.com/nalemadi/VME_CDSI_dataset_benchmark
공식 코드VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond
https://github.com/natezhenghy/udke
공식 코드Unfolded Deep Kernel Estimation for Blind Image Super-resolution
https://github.com/nathan66666/dvmsr
공식 코드DVMSR: Distillated Vision Mamba for Efficient Super-Resolution
https://github.com/nathan66666/hasn
공식 코드HASN: Hybrid Attention Separable Network for Efficient Image Super-resolution
https://github.com/ncarraz/ESRGANplus
공식 코드ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network
https://github.com/ncherel/psal
공식 코드https://github.com/ngchc/CameraSR
공식 코드https://github.com/nikhilvravi/DukeSR
공식 코드https://github.com/niopeng/CHIMLE
공식 코드CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis
https://github.com/nithin-gk/at-ddpm
공식 코드https://github.com/nju-jet/fmen
공식 코드Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution
https://github.com/njulj/AdaDM
공식 코드https://github.com/njulj/RFDN
공식 코드Residual Feature Distillation Network for Lightweight Image Super-Resolution
https://github.com/nmhkahn/PCARN-pytorch
공식 코드Efficient Deep Neural Network for Photo-realistic Image Super-Resolution
https://github.com/nonwhy/pure
공식 코드https://github.com/ofsoundof/dhp
공식 코드https://github.com/ofsoundof/grl-image-restoration
공식 코드Efficient and Explicit Modelling of Image Hierarchies for Image Restoration
https://github.com/ofsoundof/imdn
공식 코드NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
https://github.com/ofsoundof/learning_filter_basis
공식 코드Learning Filter Basis for Convolutional Neural Network Compression
https://github.com/ofsoundof/ntire2022_esr
공식 코드NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
https://github.com/ohayonguy/PMRF
공식 코드Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration
https://github.com/oldsweet/hsrmamba
공식 코드HSRMamba: Efficient Wavelet Stripe State Space Model for Hyperspectral Image Super-Resolution
https://github.com/omaralezaby/mimicked_ali
공식 코드Enhanced Super-Resolution Training via Mimicked Alignment for Real-World Scenes
https://github.com/open-mmlab/mmediting
공식 코드GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond
https://github.com/pancakewaffles/metasrgan-tutorial
공식 코드https://github.com/passerer/hpinet
공식 코드From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.