🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
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Updated
Mar 12, 2024 - Python
🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
SwinIR: Image Restoration Using Swin Transformer (official repository)
[CVPR'20] TTSR: Learning Texture Transformer Network for Image Super-Resolution
[ECCV] Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration. Advances in Image Manipulation (AIM) workshop ECCV 2022. Try it out! over 3.3M runs https://replicate.com/mv-lab/swin2sr
PyTorch implementation of Image Super-Resolution Using Deep Convolutional Networks (ECCV 2014)
ACG2vec (Anime Comics Games to vector) are committed to creating a playground that combines ACG and Deep learning.(文本语义检索、以图搜图、语义搜图、图片超分辨率、推荐系统)
TensorFlow JS models for MIRNet for low-light💡 image enhancement
A Flexible and Unified Image Restoration Framework (PyTorch), including state-of-the-art image restoration model. Such as NAFNet, Restormer, MPRNet, MIMO-UNet, SCUNet, SwinIR, HINet, etc. ⭐⭐⭐⭐⭐⭐
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network implemented in Keras
Lightweight Image Super-Resolution with Enhanced CNN (Knowledge-Based Systems,2020)
PyTorch implementation of Accelerating the Super-Resolution Convolutional Neural Network (ECCV 2016)
Official PyTorch code for Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow, ICCV2021)
Camera Lens Super-Resolution in CVPR 2019
Code for Non-Local Recurrent Network for Image Restoration (NeurIPS 2018)
Official PyTorch code for Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021)
Image super resolution models for PyTorch.
A Collection of Low Level Vision Research Groups
Official PyTorch code for Flow-based Kernel Prior with Application to Blind Super-Resolution (FKP, CVPR2021)
Pytorch implement: Residual Dense Network for Image Super-Resolution
Simultaneous Enhancement and Super-Resolution. #RSS2020
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