https://github.com/chunmeifeng/T2Net
공식 코드Task Transformer Network for Joint MRI Reconstruction and 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/chxy95/hat
공식 코드Activating More Pixels in Image Super-Resolution Transformer
https://github.com/cindydeng1991/Wavelet-Domain-Style-Transfer-for-an-Effective-Perception-distortion-Tradeoff-in-Single-Image-Super-
공식 코드https://github.com/ckyrkou/step_models
공식 코드Toward Efficient Convolutional Neural Networks With Structured Ternary Patterns
https://github.com/claroche-r/dmbsr
공식 코드https://github.com/claudiom4sir/stablevsr
공식 코드https://github.com/clovaai/cutblur
공식 코드Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
https://github.com/cmla/PROBAVref
공식 코드Proba-V-ref: Repurposing the Proba-V challenge for reference-aware super resolution
https://github.com/conor-horgan/spectrai
공식 코드https://github.com/crispyfeso4/anysr
공식 코드AnySR: Realizing Image Super-Resolution as Any-Scale, Any-Resource
https://github.com/csdwren/PReNet
공식 코드Progressive Image Deraining Networks: A Better and Simpler Baseline
https://github.com/csguoh/lemma
공식 코드Towards Robust Scene Text Image Super-resolution via Explicit Location Enhancement
https://github.com/csguoh/mambair
공식 코드MambaIR: A Simple Baseline for Image Restoration with State-Space Model
https://github.com/csjliang/dasr
공식 코드Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution
https://github.com/csjliang/ldl
공식 코드Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution
https://github.com/csmliu/AdaDSR
공식 코드Deep Adaptive Inference Networks for Single Image Super-Resolution
https://github.com/csslc/ccsr
공식 코드Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-Resolution
https://github.com/csslc/pisa-sr
공식 코드Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach
https://github.com/cswin/RC-Nets
공식 코드Image Restoration Using Deep Regulated Convolutional Networks
https://github.com/cswry/osediff
공식 코드One-Step Effective Diffusion Network for Real-World Image Super-Resolution
https://github.com/cswry/seesr
공식 코드SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution
https://github.com/csxmli2016/GFRNet
공식 코드https://github.com/csxmli2016/marconet
공식 코드Learning Generative Structure Prior for Blind Text Image Super-resolution
https://github.com/csxmli2016/redegnet
공식 코드From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution
https://github.com/cszn/BSRGAN
공식 코드Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
https://github.com/cszn/DPSR
공식 코드Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels
https://github.com/cszn/DnCNN
공식 코드Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/cszn/KAIR
공식 코드AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
https://github.com/cszn/SRMD
공식 코드Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
https://github.com/cszn/USRNet
공식 코드https://github.com/cszn/ircnn
공식 코드https://github.com/cuhk-aim-group/star-rl
공식 코드https://github.com/cv-junchengli/sisr-survey
공식 코드A Systematic Survey of Deep Learning-based Single-Image Super-Resolution
https://github.com/dandingbudanding/BAM
공식 코드BAM: A Balanced Attention Mechanism for Single Image Super Resolution
https://github.com/danfenghong/ECCV2020_CUCaNet
공식 코드Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution
https://github.com/danobohud/TransferLearningforPSC
공식 코드Transfer Learning for Protein Structure Classification at Low Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.