Practical Single-Image Super-Resolution Using Look-Up Table

Related tags

Deep LearningSR-LUT
Overview

Practical Single-Image Super-Resolution Using Look-Up Table

[Paper]

Dependency

  • Python 3.6
  • PyTorch
  • glob
  • numpy
  • pillow
  • tqdm
  • tensorboardx

1. Training deep SR network

  1. Move into a directory.
cd ./1_Train_deep_model
  1. Prepare DIV2K training images into ./train.
  • HR images should be placed as ./train/DIV2K_train_HR/*.png.
  • LR images should be placed as ./train/DIV2K_train_LR_bicubic/X4/*.png.
  1. Set5 HR/LR validation png images are already included in ./val, or you can use other images.

  2. You may modify user parameters in L22 in ./Train_Model_S.py.

  3. Run.

python Train_Model_S.py
  1. Checkpoints will be saved in ./checkpoint/S.
  • Training log will be generated in ./log/S.

2. Transferring to LUT

  1. Move into a directory.
cd ./2_Transfer_to_LUT
  1. Modify user parameters in L9 in ./Transfer_Model_S.py.
  • Specify a saved checkpoint in the step 1, or you can use attached ./Model_S.pth.
  1. Run.
python Transfer_Model_S.py
  1. The resulting LUT will be saved like ./Model_S_x4_4bit_int8.npy.

3. Testing using LUT

  1. Move into a directory.
cd ./3_Test_using_LUT
  1. Modify user parameters in L17 in ./Test_Model_S.py.
  • Specify the generated LUT in the step 2, or use attached LUTs (npy files).
  1. Set5 HR/LR test images are already included in ./test, or you can use other images.

  2. Run.

python Test_Model_S.py      # Ours-S
python Test_Model_F.py      # Ours-F
python Test_Model_V.py      # Ours-V
  1. Resulting images will be saved in ./output_S_x4_4bit/*.png.

  2. We can reproduce the results of Table 6 in the paper, by modifying the variable SAMPLING_INTERVAL in L19 in Test_Model_S.py to range 3-8.

4. Testing on a smartphone

  1. Download SR-LUT.apk and install it.

  2. You can test Set14 images or other images.

SR-LUT Android app demo

BibTeX

@InProceedings{jo2021practical,
   author = {Jo, Younghyun and Kim, Seon Joo},
   title = {Practical Single-Image Super-Resolution Using Look-Up Table},
   booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
   month = {June},
   year = {2021}
}
Owner
Younghyun Jo
Younghyun Jo
code for our paper "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer"

SHOT++ Code for our TPAMI submission "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer" that is ext

75 Dec 16, 2022
The Balloon Learning Environment - flying stratospheric balloons with deep reinforcement learning.

Balloon Learning Environment Docs The Balloon Learning Environment (BLE) is a simulator for stratospheric balloons. It is designed as a benchmark envi

Google 87 Dec 25, 2022
Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper

AnimeGAN - Deep Convolutional Generative Adverserial Network PyTorch implementation of DCGAN introduced in the paper: Unsupervised Representation Lear

Rohit Kukreja 23 Jul 21, 2022
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation

Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation Introduction This is a PyTorch

XMed-Lab 30 Sep 23, 2022
Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch

Bootstrap Your Own Latent (BYOL), in Pytorch Practical implementation of an astoundingly simple method for self-supervised learning that achieves a ne

Phil Wang 1.4k Dec 29, 2022
Simple streamlit app to demonstrate HERE Tour Planning

Table of Contents About the Project Built With Getting Started Prerequisites Installation Usage Roadmap Contributing License Acknowledgements About Th

Amol 8 Sep 05, 2022
NCNN implementation of Real-ESRGAN. Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration.

NCNN implementation of Real-ESRGAN. Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration.

Xintao 593 Jan 03, 2023
Adaptive Denoising Training (ADT) for Recommendation.

DenoisingRec Adaptive Denoising Training for Recommendation. This is the pytorch implementation of our paper at WSDM 2021: Denoising Implicit Feedback

Wenjie Wang 51 Dec 30, 2022
The source code for CATSETMAT: Cross Attention for Set Matching in Bipartite Hypergraphs

catsetmat The source code for CATSETMAT: Cross Attention for Set Matching in Bipartite Hypergraphs To be able to run it, add catsetmat to PYTHONPATH H

2 Dec 19, 2022
The Generic Manipulation Driver Package - Implements a ROS Interface over the robotics toolbox for Python

Armer Driver Armer aims to provide an interface layer between the hardware drivers of a robotic arm giving the user control in several ways: Joint vel

QUT Centre for Robotics (QCR) 13 Nov 26, 2022
Code for Boundary-Aware Segmentation Network for Mobile and Web Applications

BASNet Boundary-Aware Segmentation Network for Mobile and Web Applications This repository contain implementation of BASNet in tensorflow/keras. comme

Hamid Ali 8 Nov 24, 2022
A video scene detection algorithm is designed to detect a variety of different scenes within a video

Scene-Change-Detection - A video scene detection algorithm is designed to detect a variety of different scenes within a video. There is a very simple definition for a scene: It is a series of logical

1 Jan 04, 2022
A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).

Attention Walk ⠀⠀ A PyTorch Implementation of Watch Your Step: Learning Node Embeddings via Graph Attention (NIPS 2018). Abstract Graph embedding meth

Benedek Rozemberczki 303 Dec 09, 2022
Piotr - IoT firmware emulation instrumentation for training and research

Piotr: Pythonic IoT exploitation and Research Introduction to Piotr Piotr is an emulation helper for Qemu that provides a convenient way to create, sh

Damien Cauquil 51 Nov 09, 2022
A repo to show how to use custom dataset to train s2anet, and change backbone to resnext101

A repo to show how to use custom dataset to train s2anet, and change backbone to resnext101

jedibobo 3 Dec 28, 2022
Code of paper "Compositionally Generalizable 3D Structure Prediction"

Compositionally Generalizable 3D Structure Prediction In this work, We bring in the concept of compositional generalizability and factorizes the 3D sh

Songfang Han 30 Dec 17, 2022
pq is a jq-like Pickle file viewer

pq PQ is a jq-like viewer/processing tool for pickle files. howto # pq '' file.pkl {'other': 456, 'test': 123} # pq 'table' file.pkl |other|test| | 45

3 Mar 15, 2022
Tutorial page of the Climate Hack, the greatest hackathon ever

Tutorial page of the Climate Hack, the greatest hackathon ever

UCL Artificial Intelligence Society 12 Jul 02, 2022
This is the code of NeurIPS'21 paper "Towards Enabling Meta-Learning from Target Models".

ST This is the code of NeurIPS 2021 paper "Towards Enabling Meta-Learning from Target Models". If you use any content of this repo for your work, plea

Su Lu 7 Dec 06, 2022
Human Pose Detection on EdgeTPU

Coral PoseNet Pose estimation refers to computer vision techniques that detect human figures in images and video, so that one could determine, for exa

google-coral 476 Dec 31, 2022