image-deblurring-using-deep-learning

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image-deblurring-using-deep-learning

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

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// repository documentation

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# 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).

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