Alternatives and similar repositories for Knowledge-Distillation-for-Super-resolution:

Users that are interested in Knowledge-Distillation-for-Super-resolution are comparing it to the libraries listed below

• ofsoundof / NTIRE2022_ESR
  Solution of the NTIRE 2022 Challenge on Efficient Super-Resolution
  ☆123Updated 2 years ago
• Algolzw / EBSR
  Pytorch code for "EBSR: Feature Enhanced Burst Super-Resolution with Deformable Alignment", CVPRW 2021, 1st NTIRE (real data track).
  ☆96Updated 2 years ago
• xiezw5 / Component-Divide-and-Conquer-for-Real-World-Image-Super-Resolution
  ☆194Updated 4 years ago
• NJU-Jet / FMEN
  Lowest memory consumption and second shortest runtime in NTIRE 2022 challenge on Efficient Super-Resolution
  ☆76Updated 3 years ago
• JingyunLiang / MANet
  Official PyTorch code for Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021)
  ☆172Updated 3 years ago
• JingyunLiang / FKP
  Official PyTorch code for Flow-based Kernel Prior with Application to Blind Super-Resolution (FKP, CVPR2021)
  ☆148Updated 3 years ago
• AbdulMoqeet / MAFFSRN
  ☆46Updated 3 years ago
• TencentARC / RepSR
  Codes for "RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch Normalization"
  ☆76Updated 2 years ago
• IanYeung / RealVSR
  Dataset and Code for ICCV 2021 paper "Real-world Video Super-resolution: A Benchmark Dataset and A Decomposition based Learning Scheme"
  ☆117Updated 3 years ago
• zhjy2016 / SPLUT
  Official code for ECCV2022 paper: Learning Series-Parallel Lookup Tables for Efficient Image Super-Resolution
  ☆52Updated 2 years ago
• Alan-xw / RealSR
  [Unofficial implementation] Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model (ICCV 2019)
  ☆83Updated 3 years ago
• njulj / RFANet
  Residual Feature Aggregation Network for Image Super-Resolution
  ☆105Updated 4 years ago
• fenglinglwb / EDT
  On Efficient Transformer-Based Image Pre-training for Low-Level Vision
  ☆132Updated 2 years ago
• SYSU-SAIL / SMSR
  [CVPR 2021] Exploring Sparsity in Image Super-Resolution for Efficient Inference
  ☆239Updated 3 years ago
• ofsoundof / IMDN
  ☆50Updated 3 years ago
• NJU-Jet / SR_Framework
  A generic framework which implements some famouts super-resolution models
  ☆66Updated 3 years ago
• bytedance / RLFN
  Winner of runtime track in NTIRE 2022 challenge on Efficient Super-Resolution
  ☆161Updated 2 years ago
• aasharma90 / UnprocessDenoising_PyTorch
  Unofficial PyTorch code for the paper - Unprocessing Images for Learned Raw Denoising, CVPR'19
  ☆177Updated 4 years ago
• zudi-lin / rcan-it
  Revisiting RCAN: Improved Training for Image Super-Resolution
  ☆94Updated 2 years ago
• NJU-Jet / SR_Mobile_Quantization
  Winner solution of mobile AI (CVPRW 2021).
  ☆151Updated 2 years ago
• goutamgmb / NTIRE21_BURSTSR
  ☆90Updated last year
• zeyuxiao1997 / EDPN
  ☆82Updated 3 years ago
• chxy95 / HDRUNet
  CVPR2021 Workshop - HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization.
  ☆166Updated 2 years ago
• sanghyun-son / bicubic_pytorch
  A PyTorch implementation of widely-used bicubic interpolation, which is fully compatible with MATLAB.
  ☆138Updated 4 years ago
• yhjo09 / SR-LUT
  ☆181Updated 10 months ago
• dvlab-research / MASA-SR
  MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution (CVPR2021)
  ☆166Updated 3 years ago
• greatlog / UnpairedSR
  This is an offical implementation of the CVPR2022's paper [Learning the Degradation Distribution for Blind Image Super-Resolution](https:…
  ☆167Updated 3 years ago
• aiff22 / MAI-2021-Workshop
  MAI 2021 Workshop
  ☆42Updated 4 years ago
• ShuhangGu / DASR
  Training and Testing codes for our paper "Real-world Image Super-resolution via Domain-distance Aware Training"
  ☆236Updated 3 years ago
• junpan19 / RSDN
  Code for paper "Video Super-Resolution with Recurrent Structure-Detail Network" . The code is under preparation and is coming soon.
  ☆89Updated 4 years ago