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Aug 16, 2018
Aug 16, 2018
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Aug 16, 2018

deep_blur_detection_and_classification

Tensorflow implementation of "Defocus and Motion Blur Detection with Deep Contextual Features"

For image examples:

input2 output2

This repository contains a test code and sythetic dataset, which consists of scenes including motion and defocus blurs together in each scene.


Prerequisites (tested)

  • Ubuntu 16.04
  • Tensorflow 1.6.0 (<= 1.9.0)
  • Tensorlayer 1.8.2
  • OpenCV2

Train Details

  • We used CUHK blur detection dataset for training our network and generating our synthetic dataset
  • Train and test set lists are uploaded in 'dataset' folder
  • Need to modify some options and paths in 'main.py' and 'config.py' for training

Test Details

  • download model weights from google drive and save the model into 'model' folder.
  • specify a path of input folder in 'main.py' at line #39
  • run 'main.py'
python main.py

Synthetic Dataset

  • download synthetic train set(337MB) and synthetic test set(11.5MB) from google drive
  • Note that sharp pixels, motion-blurred pixels, and defocus-blurred pixels in GT blur maps are labeled as 0, 100, and 200, respectively, in the [0,255] range.

License

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

Any exemptions to these terms requires 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 it. In addition,ersonalization technology through userreference analysis is under study.

Please checkout out other Coupe repositories in our Posgraph github organization.

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End-to-end network for blur detection and classification (presented at PG 2018)

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