https://github.com/sovit-123/image-deblurring-using-deep-learning

PyTorch implementation of image deblurring using deep learning. Use a simple convolutional autoencoder neural network to deblur Gaussian blurred images.
https://github.com/sovit-123/image-deblurring-using-deep-learning

computer-vision computer-vision-neural-networks convolutional-neural-networks deep-learning deep-neural-networks deeplearning-image-deblur image-deblur image-deblurring machine-learning neural-networks pytorch

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PyTorch implementation of image deblurring using deep learning. Use a simple convolutional autoencoder neural network to deblur Gaussian blurred images.

• Host: GitHub
• URL: https://github.com/sovit-123/image-deblurring-using-deep-learning
• Owner: sovit-123
• Created: 2020-04-25T02:17:16.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-02-16T09:24:50.000Z (almost 2 years ago)
• Last Synced: 2023-03-05T11:12:41.259Z (almost 2 years ago)
• Topics: computer-vision, computer-vision-neural-networks, convolutional-neural-networks, deep-learning, deep-neural-networks, deeplearning-image-deblur, image-deblur, image-deblurring, machine-learning, neural-networks, pytorch
• Language: Jupyter Notebook
• Homepage:
• Size: 3.75 MB
• Stars: 13
• Watchers: 3
• Forks: 4
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# README

* **First of all, you can find the dataset on Kaggle:**

  * **Dataset  => https://www.kaggle.com/kwentar/blur-dataset.**

* Get the dataset and extract it inside the `input` folder. Following is the directory structure for the project:

  ```

  ├───input

  │   ├───defocused_blurred

  │   ├───gaussian_blurred

  │   ├───motion_blurred

  │   └───sharp

  ├───outputs

  │   └───saved_images

  └───src

  ```

## Steps to Execute

* I have not used the blurred images that are given in the original dataset for image deblurring. They are spatially variant due to motion blurring and defocus-blurring. I have added Gaussian blurring to the images using the `add_guassian_blur.py` script inside the `src` folder. Then I have used these images for deblurring.

* **The following is the order of execution:**

  1. `add_gaussian_blur.py`

  2. `deblur_ae.py`

* ***Note: Execute all the scripts while being within the `src` folder inside the terminal***.

## Some Results

* **Loss Plot**

  ![](https://github.com/sovit-123/image-deblurring-using-deep-learning/blob/master/outputs/loss.png?raw=true)

* **Blurred Image**

  ![](https://github.com/sovit-123/image-deblurring-using-deep-learning/blob/master/outputs/saved_images/blur0.jpg?raw=true)

  **Final Deblurred Image**

  ![](https://github.com/sovit-123/image-deblurring-using-deep-learning/blob/master/outputs/saved_images/val_deblurred39.jpg?raw=true)

## Future Work

* To deblur the spatially variant images inside the `defocused_blurred` and `motion_blurred` folders.

* Add more and better models to `models.py` script. 

* **Any useful contribution to the project is highly appreciated.**

## References

* Paper: Image Deblurring with BlurredNoisy Image Pairs, **Lu Yuan, Jian Sun, Long Quan, Heung-Yeung Shum.**

* [Image super-resolution as sparse representation of raw image patches](https://www.researchgate.net/publication/221364186_Image_super-resolution_as_sparse_representation_of_raw_image_patches), **Jianchao Yang†, John Wright‡, Yi Ma‡, Thomas Huang†**.

* mage Deblurring and Super-Resolution Using Deep Convolutional Neural Networks](https://www.researchgate.net/publication/328985265_Image_Deblurring_and_Super-Resolution_Using_Deep_Convolutional_Neural_Networks), **Fatma Albluwi, Vladimir A. Krylov & Rozenn Dahyot**.

  * [GitHub Code](https://github.com/YapengTian/SRCNN-Keras).