https://gitee.com/wen--he/AMRSR
공식 코드Attention-based Multi-Reference Learning for 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/2minkyulee/aesop-auto-encoded-supervision-for-perceptual-image-super-resolution
공식 코드Auto-Encoded Supervision for Perceptual Image Super-Resolution
https://github.com/2minkyulee/eco
공식 코드Noise-free Optimization in Early Training Steps for Image Super-Resolution
https://github.com/AMD-AIG-AIMA/DoSSR
공식 코드Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEs
https://github.com/ARM-software/sesr
공식 코드Collapsible Linear Blocks for Super-Efficient Super Resolution
https://github.com/AbdulMoqeet/HRAN
공식 코드Hybrid Residual Attention Network for Single Image Super Resolution
https://github.com/Aitical/MCLIR
공식 코드Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration
https://github.com/Aitical/PCL-SISR
공식 코드A Practical Contrastive Learning Framework for Single-Image Super-Resolution
https://github.com/Amirhossein-Nayebi/Nordic-Vehicle-Dataset
공식 코드https://github.com/ArshockAbedan/CS_MSc_Codes
공식 코드Multi-Modality Image Super-Resolution using Generative Adversarial Networks
https://github.com/BookerDeWitt/GFN-IJCV
공식 코드https://github.com/Booooooooooo/CSD
공식 코드Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
https://github.com/Brian-Moser/zido
공식 코드https://github.com/ChaofWang/AWSRN
공식 코드Lightweight Image Super-Resolution with Adaptive Weighted Learning Network
https://github.com/Cheeun/AdaBM
공식 코드AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-Resolution
https://github.com/Cheeun/DAQ-pytorch
공식 코드DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks
https://github.com/Chernobyllight/ReDSR
공식 코드Preserving Full Degradation Details for Blind Image Super-Resolution
https://github.com/Ding-Liu/NLRN
공식 코드https://github.com/ElementAI/HighRes-net
공식 코드HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
https://github.com/EquationWalker/CATANet
공식 코드CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-Resolution
https://github.com/EscVM/RAMS
공식 코드https://github.com/Eulring/Hybrid-Function-Sparse-Representation
공식 코드Hybrid Function Sparse Representation towards Image Super Resolution
https://github.com/FarihaHossain/SwinFSR
공식 코드https://github.com/GinZhu/RDST
공식 코드https://github.com/GreyCC/DTLS
공식 코드Domain Transfer in Latent Space (DTLS) Wins on Image Super-Resolution -- a Non-Denoising Model
https://github.com/GuYuanjie/DeepFusionPrior
공식 코드Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus Imaging
https://github.com/GuangluDong0728/SFD
공식 코드https://github.com/HDCVLab/EDFace-Celeb-1M
공식 코드EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale Dataset
https://github.com/Hammour-steak/GOUB
공식 코드Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge
https://github.com/HarleyHK/FARF
공식 코드https://github.com/HarleyHK/JMPF
공식 코드Joint Maximum Purity Forest with Application to Image Super-Resolution
https://github.com/HengRuiZ/brelu
공식 코드Efficient Integer-Arithmetic-Only Convolutional Neural Networks
https://github.com/Holmes-Alan/ABPN
공식 코드Image Super-Resolution via Attention based Back Projection Networks
https://github.com/Holmes-Alan/HBPN
공식 코드Hierarchical Back Projection Network for Image Super-Resolution
https://github.com/Holmes-Alan/RefVAE
공식 코드Variational AutoEncoder for Reference based Image Super-Resolution
https://github.com/Holmes-Alan/dSRVAE
공식 코드Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.