image-deblurring-performance-analysis

Name: Statistical Analysis of Image Deblurring Methods Performance Across Classical and AI-Based Approaches

In the field of image processing, the degradation of image quality due to various types of distorions poses significant challenges for both human interpretation and machine learning applications. This project aims to systematically investigate blur image distortions by statistically analyzing relationships between parameters of the blurred images and metrics of the deblurring methods, both classical and AI-based.

Dataset is availiable on [HuggingFace]

Getting Started

Note

Please make sure you have conda installed on your system.

1. Clone the Repository:

   git clone https://github.com/slymachenko/image-deblurring-performance-analysis.git
   cd image-deblurring-performance-analysis

2. Create Conda environment using file environment.yml:

   • For Dataset Creation part:

   conda create --file notebooks/dataset_creation/environment.yml
   conda activate idpa-creation

   • For Dataset Analysis part:

   conda create --file notebooks/dataset_analysis/environment.yml
   conda activate idpa-analysis

3. Download dataset from HuggingFace: [Link]

   Follow the file structure of the HuggingFace repository with the root of it being data/image-deblurring-performance-analysis/

4. Run the desired script/notebook in the conda environment.

Credits

Parts of this project were inspired by or copied from the following sources:

• MPRNet repository
• DeblurGAN-v2 repository

License

The project is released under the MIT license.