超分辨率相关资源大列表-2

本文收藏自Github，仅供学习交流所用。

Video Super Resolution

A collection of state-of-the-art video or single-image super-resolution architectures, reimplemented in tensorflow.

Pretrained weights is uploading now.

Several referenced PyTorch implementations are also included now.

Quick Link: Installation Getting Started

Network list and reference (Updating)

The hyperlink directs to paper site, follows the official codes if the authors open sources.

All these models are implemented in ONE framework.

*The 1st repo is by paper author.

**Y: included; -: not included; T: under-testing.

You can download pre-trained weights through prepare_data, or visit the hyperlink at √.

Link of datasets

(please contact me if any of links offend you or any one disabled)

Other open datasets: Kaggle ImageNet COCO

VSR package

This package offers a training and data processing framework based on TF. What I made is a simple, easy-to-use framework without lots of encapulations and abstractions. Moreover, VSR can handle raw NV12/YUV as well as a sequence of images as inputs.

Install

1. Prepare proper tensorflow and pytorch(optional). For example, GPU and CUDA10.0 (recommend to use conda):

   
   conda install tensorflow-gpu==1.12.0
   # optional
   # conda install pytorch==1.0.1  

2. Install VSR package

   
   # For someone see this doc online
   # git clone https://github.com/loseall/VideoSuperResolution && cd VideoSuperResolution
   pip install -e .  

Getting Started

1. Download pre-trained weights and (optinal) training datasets. For instance, let's begin with VESPCN and vid4 test data:

   
   python prepare_data.py --filter vespcn vid4  

2. Evaluate

   
   cd Train
   python run.py --model vespcn --test vid4  

3. Train

   
   python prepare_data.py --filter mcl-v
   cd Train
   python run.py --model vespcn --dataset mcl-v --memory_limit 1GB --epochs 100  

OK, that's all you need. For more details, use --help to get more information.

To train/test/infer any other model in VSR.Models, please see README. To write and train your own model via VSR, please see Docs.