[ICCV 2021] Official Tensorflow Implementation for "Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions"

Related tags

Deep Learning cnn-model deblurring defocus-deblurring
Overview

KPAC: Kernel-Sharing Parallel Atrous Convolutional block

License CC BY-NC

This repository contains the official Tensorflow implementation of the following paper:

Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions
Hyeongseok Son, Junyong Lee, Sunghyun Cho, Seungyong Lee, ICCV 2021

Getting Started

Prerequisites

Tested environment

Ubuntu16.04 Python 2.7.12 Tensorflow 1.10.0 CUDA 9.0

  1. Pre-trained models

     ```
 ├── ./pretrained
 │   ├── single_2level.npz
 │   ├── single_3level.npz
 │   ├── dual.npz
 ```

  2. Built-in Tensorlayer (modified from Tensorlayer 1.7.0)

     ```
 ├── ./tensorlayer
 ```

  3. Datapath setting in config.py

Testing models of ICCV2021

# Our 2-level model 
CUDA_VISIBLE_DEVICES=0 python main_eval_2level.py

# Our 3-level model 
CUDA_VISIBLE_DEVICES=0 python main_eval_3level.py

# Our dual pixel-based model
CUDA_VISIBLE_DEVICES=0 python main_eval_dual.py

Citation

If you find this code useful, please consider citing:

@InProceedings{Son_2021_ICCV,
    author = {Son, Hyeongseok and Lee, Junyong and Cho, Sunghyun and Lee, Seungyong},
    title = {Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions},
    booktitle = {Proc. ICCV},
    year = {2021}
}

Contact

Open an issue for any inquiries. You may also have contact with [email protected]

License

This software is being made available under the terms in the LICENSE file.

Any exemptions to these terms require a license from the Pohang University of Science and Technology.

About Coupe Project

Project ‘COUPE’ aims to develop software that evaluates and improves the quality of images and videos based on big visual data. To achieve the goal, we extract sharpness, color, composition features from images and develop technologies for restoring and improving by using them. In addition, personalization technology through user reference analysis is under study.

Please checkout other Coupe repositories in our Posgraph github organization.

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Comments
  • the problem for evaluation result.

    the problem for evaluation result.

    Thank you for your excellent work! When we used the pre training model provided by you to evaluate, we found that the effect was very poor. The index of PSNR was only 5. The main problem encountered is that there are many parallel color artifacts in the evaluated image. We wondered whether it was the problem of the installation environment. The environment we have configured is as follows. Or can you provide us with pictures for your evaluation？Thank you! The environment of python and tf is based on the version you provide.

    _tflow_select 2.1.0
    absl-py 0.15.0
    astor 0.8.0
    backports 1.1
    backports.functools-lru-cache 1.6.4
    backports.weakref 1.0.post1
    blas 1.0
    c-ares 1.18.1
    ca-certificates 2022.07.19
    certifi 2020.6.20
    cloudpickle 1.3.0
    cudatoolkit 9.0
    cudnn 7.6.5
    cupti 9.0.176
    cycler 0.10.0
    decorator 4.4.2
    easydict 1.10
    enum34 1.1.6
    funcsigs 1.0.2
    futures 3.3.0
    gast 0.5.3
    grpcio 1.27.2
    intel-openmp 2020.2
    kiwisolver 1.1.0
    libffi 3.3
    libgcc-ng 11.2.0
    libgfortran-ng 7.3.0
    libprotobuf 3.11.2
    libstdcxx-ng 11.2.0
    markdown 3.1.1
    matplotlib 2.2.5
    mkl 2020.2
    mkl-service 2.3.0
    mkl_fft 1.0.15
    mkl_random 1.1.0
    mock 3.0.5
    ncurses 6.3
    networkx 2.2
    numpy 1.16.6
    numpy-base 1.16.6
    opencv-python 4.0.0.21
    openssl 1.1.1q
    Pillow 6.2.2
    pip 19.3.1
    protobuf 3.11.2
    pyparsing 2.4.7
    python 2.7.12
    python-dateutil 2.8.2
    pytz 2022.5
    PyWavelets 1.0.3
    readline 8.1.2
    scikit-image 0.14.5
    scipy 1.2.3
    setuptools 44.0.0
    six 1.16.0
    sqlite 3.39.3
    subprocess32 3.5.4
    tensorboard 1.10.0
    tensorflow 1.10.0
    tensorflow-base 1.10.0
    tensorflow-gpu 1.10.0
    termcolor 1.1.0
    tk 8.6.12
    werkzeug 1.0.1
    wheel 0.37.1
    zlib 1.2.12

    opened by qylen 4
  • Testing error

    Testing error

    Hi

    Thank you for providing the code for your paper.

    I am trying to replicate the results of the paper and would like to know which tensorflow version you use. I have tried testing with tensorflow 1.10 and run into the following error with python main_eval_3level.py

    [TL] UpSampling2dLayer sha_attention/0: is_scale:False size:[140.0, 210.0] method:1 align_corners:False
TypeError: Expected int32, got 140.0 of type 'float' instead

    Thanks

    opened by adityac8 2
  • Could you provide the model training code?

    Could you provide the model training code?

    Your work has great impacts on my thoughts about defocus deblurring, thank you for doing this amazing work! However, the python scripts in this repository are all evaluation codes for pretrained models. Is it possible for you to provide the training code? Thanks a lot!

    opened by CindyChow123 0
Owner
Hyeongseok Son
Hyeongseok Son
GitHub
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