https://github.com/hongyuanyu/span
공식 코드Swift Parameter-free Attention Network for Efficient 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/house-leo/realsr-ccl
공식 코드Criteria Comparative Learning for Real-scene Image Super-Resolution
https://github.com/house-leo/realsr-zero
공식 코드Criteria Comparative Learning for Real-scene Image Super-Resolution
https://github.com/house-leo/rwsr-edl
공식 코드Real-World Image Super-Resolution by Exclusionary Dual-Learning
https://github.com/huawei-noah/Pretrained-IPT
공식 코드https://github.com/hvision-nku/controlsr
공식 코드ControlSR: Taming Diffusion Models for Consistent Real-World Image Super Resolution
https://github.com/hvision-nku/srformer
공식 코드SRFormerV2: Taking a Closer Look at Permuted Self-Attention for Image Super-Resolution
https://github.com/i2-multimedia-lab/cdformer
공식 코드CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-Resolution
https://github.com/i2-multimedia-lab/dsat
공식 코드Degradation-Aware Self-Attention Based Transformer for Blind Image Super-Resolution
https://github.com/icandle/MAN
공식 코드Multi-scale Attention Network for Single Image Super-Resolution
https://github.com/icandle/camixersr
공식 코드https://github.com/icandle/ddistill-sr
공식 코드DDistill-SR: Reparameterized Dynamic Distillation Network for Lightweight Image Super-Resolution
https://github.com/icandle/efdn
공식 코드Edge-enhanced Feature Distillation Network for Efficient Super-Resolution
https://github.com/idearibosome/tf-bsrn-sr
공식 코드Lightweight and Efficient Image Super-Resolution with Block State-based Recursive Network
https://github.com/idiosyncracies/Sebica
공식 코드Sebica: Lightweight Spatial and Efficient Bidirectional Channel Attention Super Resolution Network
https://github.com/ifnspaml/superresolutionmultiscaletraining
공식 코드A Lightweight Image Super-Resolution Transformer Trained on Low-Resolution Images Only
https://github.com/ifryed/HyperRes
공식 코드https://github.com/ihubdata-mobility/public-fgvd
공식 코드A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstrained Roads
https://github.com/imalne/pbasr
공식 코드A New Dataset and Framework for Real-World Blurred Images Super-Resolution
https://github.com/imt-project-lts-sr/mrunet-for-modis-super-resolution
공식 코드Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution
https://github.com/inverted-ai/object-detection
공식 코드https://github.com/iorism/CNN
공식 코드https://github.com/iviplab/cfin
공식 코드Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution
https://github.com/iviplab/ctcnet
공식 코드CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution
https://github.com/iviplab/fiwhn
공식 코드Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network
https://github.com/iviplab/lbnet
공식 코드https://github.com/ivrl/dsr
공식 코드https://github.com/iwuqing/arssr
공식 코드An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation
https://github.com/jaehakim97/sr4ir
공식 코드Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss
https://github.com/jaewon-lee-b/lte
공식 코드Local Texture Estimator for Implicit Representation Function
https://github.com/jasonli0707/hklut
공식 코드Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-Resolution
https://github.com/jdfxzzy/dpmn
공식 코드Improving Scene Text Image Super-resolution via Dual Prior Modulation Network
https://github.com/jdfxzzy/pean
공식 코드PEAN: A Diffusion-Based Prior-Enhanced Attention Network for Scene Text Image Super-Resolution
https://github.com/jiahong-fu/kxnet
공식 코드KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution
https://github.com/jiamian-wang/iterative-soft-shrinkage-sr
공식 코드Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution
https://github.com/jiangsutx/SPMC_VideoSR
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.