Code and models for "Rethinking Deep Image Prior for Denoising" (ICCV 2021)

Related tags

Deep LearningDIP-denosing
Overview

DIP-denosing

This is a code repo for Rethinking Deep Image Prior for Denoising (ICCV 2021).

Addressing the relationship between Deep image prior and effective degrees of freedom, DIP-SURE with STE(stochestic temporal ensemble) shows reasonable result on single image denoising.

If you use any of this code, please cite the following publication:

@article{jo2021dipdenoising,
  author  = {Yeonsik Jo, Se young chun,  and Choi, Jonghyun},
  title     = {Rethinking Deep Image Prior for Denoising},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  month     = {October},
  year      = {2021},
  pages     = {5087-5096}
}

Working environment

  • TITAN Xp
  • ubuntu 18.04.4
  • pytorch 1.6

Note: Experimental results were not checked in other environments.

Set-up

  • Make your own environment
conda create --name DIP --file requirements.txt
conda avtivate DIP
pip install tqdm

Inference

  • Produce CSet9 result
bash exp_denoising.sh CSet9 <GPU ID>
  • For your own data with sigma=25 setup
mkdir testset/<YOUR_DATASET>
python main.py --dip_type eSURE_new --net_type s2s --exp_tag <EXP_NAME> --optim RAdam --force_steplr --desc sigma25   denoising --sigma 25 --eval_data <YOUR_DATASET>

Browsing experimental result

  • We provide reporting code with invoke.
invoke showtable csv/<exp_type>/<exp_tag>
  • Example.
invoke showtable csv/poisson/MNIST/
PURE_dc_scale001_new                     optimal stopping : 384.30,     31.97/0.02      | ZCSC : 447.60,         31.26/0.02 | STE 31.99/0.02
PURE_dc_scale01_new                      optimal stopping : 94.70,      24.96/0.12      | ZCSC : 144.60,         24.04/0.14 | STE 24.89/0.12
PURE_dc_scale02_new                      optimal stopping : 70.30,      22.92/0.20      | ZCSC : 110.00,         21.82/0.22 | STE 22.83/0.20
<EXEPRIMENTAL NAME>                      optimal stopping :<STEP>,      <PSNR>/<LPIPS>  | ZCSC : <STEP>,      <PSNR>/<LPIPS>| STE <PSNR>/<LPIPS>

The reported numbers are PSNR/LPIPS.

Results in paper

For the result used on paper, please refer this link.

SSIM score

For SSIM score of color images, I used matlab code same as the author of S2S.
This is the demo code I received from the S2S author.
Thank you Mingqin!

% examples
ref = im2double(imread('gt.png'));
noisy = im2double(imread('noisy.png'));
psnr_result = psnr(ref, noisy);
ssim_result = ssim(ref, noisy);

License

MIT license.

Contacts

For questions, please send an email to [email protected]

Owner
Computer Vision Lab. @ GIST
Some useful codes for computer vision and machine learning.
Computer Vision Lab. @ GIST
GitHub Repository
NeuralForecast is a Python library for time series forecasting with deep learning models

NeuralForecast is a Python library for time series forecasting with deep learning models. It includes benchmark datasets, data-loading utilities, evaluation functions, statistical tests, univariate m

Nixtla 1.1k Jan 03, 2023
A Pytorch Implementation of a continuously rate adjustable learned image compression framework.

GainedVAE A Pytorch Implementation of a continuously rate adjustable learned image compression framework, Gained Variational Autoencoder(GainedVAE). N

39 Dec 24, 2022
ByteTrack超详细教程！训练自己的数据集&&摄像头实时检测跟踪

ByteTrack超详细教程！训练自己的数据集&&摄像头实时检测跟踪

Double-zh 45 Dec 19, 2022
Code accompanying our NeurIPS 2021 traffic4cast challenge

Traffic forecasting on traffic movie snippets This repo contains all code to reproduce our approach to the IARAI Traffic4cast 2021 challenge. In the c

Nina Wiedemann 2 Aug 09, 2022
Swin-Transformer is basically a hierarchical Transformer whose representation is computed with shifted windows.

Swin-Transformer Swin-Transformer is basically a hierarchical Transformer whose representation is computed with shifted windows. For more details, ple

旷视天元 MegEngine 9 Mar 14, 2022
This repository is all about spending some time the with the original problem posed by Minsky and Papert

This repository is all about spending some time the with the original problem posed by Minsky and Papert. Working through this problem is a great way to begin learning computer vision.

Jaissruti Nanthakumar 1 Jan 23, 2022
Official code for our ICCV paper: "From Continuity to Editability: Inverting GANs with Consecutive Images"

GANInversion_with_ConsecutiveImgs Official code for our ICCV paper: "From Continuity to Editability: Inverting GANs with Consecutive Images" https://a

QingyangXu 38 Dec 07, 2022
A Comparative Framework for Multimodal Recommender Systems

Cornac Cornac is a comparative framework for multimodal recommender systems. It focuses on making it convenient to work with models leveraging auxilia

Preferred.AI 671 Jan 03, 2023
Methods to get the probability of a changepoint in a time series.

Bayesian Changepoint Detection Methods to get the probability of a changepoint in a time series. Both online and offline methods are available. Read t

Johannes Kulick 554 Dec 30, 2022
Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets. Predict remaining-useful-life (RUL).

Knowledge Informed Machine Learning using a Weibull-based Loss Function Exploring the concept of knowledge-informed machine learning with the use of a

Tim 43 Dec 14, 2022
Deep Learning (with PyTorch)

Deep Learning (with PyTorch) This notebook repository now has a companion website, where all the course material can be found in video and textual for

Alfredo Canziani 6.2k Jan 07, 2023
Code repository for paper `Skeleton Merger: an Unsupervised Aligned Keypoint Detector`.

Skeleton Merger Skeleton Merger, an Unsupervised Aligned Keypoint Detector. The paper is available at https://arxiv.org/abs/2103.10814. A map of the r

北海若 48 Nov 14, 2022
Who calls the shots? Rethinking Few-Shot Learning for Audio (WASPAA 2021)

rethink-audio-fsl This repo contains the source code for the paper "Who calls the shots? Rethinking Few-Shot Learning for Audio." (WASPAA 2021) Table

Yu Wang 34 Dec 24, 2022
SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

SalGAN: Visual Saliency Prediction with Adversarial Networks Junting Pan Cristian Canton Ferrer Kevin McGuinness Noel O'Connor Jordi Torres Elisa Sayr

Image Processing Group - BarcelonaTECH - UPC 347 Nov 22, 2022
Evaluation toolkit of the informative tracking benchmark comprising 9 scenarios, 180 diverse videos, and new challenges.

Informative-tracking-benchmark Informative tracking benchmark (ITB) higher diversity. It contains 9 representative scenarios and 180 diverse videos. m

Xin Li 15 Nov 26, 2022
Lua-parser-lark - An out-of-box Lua parser written in Lark

An out-of-box Lua parser written in Lark Such parser handles a relaxed version o

Taine Zhao 2 Jul 19, 2022
Sandbox for training deep learning networks

Deep learning networks This repo is used to research convolutional networks primarily for computer vision tasks. For this purpose, the repo contains (

Oleg Sémery 2.7k Jan 01, 2023
Learning Confidence for Out-of-Distribution Detection in Neural Networks

Learning Confidence Estimates for Neural Networks This repository contains the code for the paper Learning Confidence for Out-of-Distribution Detectio

235 Jan 05, 2023
Hyperbolic Image Segmentation, CVPR 2022

Hyperbolic Image Segmentation, CVPR 2022 This is the implementation of paper Hyperbolic Image Segmentation (CVPR 2022). Repository structure assets :

Mina Ghadimi Atigh 46 Dec 29, 2022
QRec: A Python Framework for quick implementation of recommender systems (TensorFlow Based)

Introduction QRec is a Python framework for recommender systems (Supported by Python 3.7.4 and Tensorflow 1.14+) in which a number of influential and

Yu 1.4k Dec 30, 2022