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Image Restoration: Breathing Life into Old Memories

Updated: Oct 20, 2023


Photo restoration in the field of computer vision refers to the process of recovering an image that has been degraded by various factors, returning it to its original or near-original state.

Causes of Image Degradation:

a. Physical damage: Tears, scratches, and folds on photos, especially printed ones.

b. Environmental factors: Water damage, mold, stains, and discoloration due to sunlight or chemicals.

c. Age: Fading over time, especially for older photos.

d. Digital artifacts: Noise, blur due to motion or out-of-focus, or compression artifacts.

e. Others: Over or under-exposure, color casts, or dust and dirt.

Here are some of the prominent applications of image restoration:


  • Enhancing old or damaged photographs.

  • Correcting motion blur, defocus blur, or other artifacts in digital photography.

Medical Imaging:

  • Enhancing MRI, CT, X-ray, or ultrasound images by removing noise or artifacts.

  • Improving clarity and readability of medical images for better diagnosis.

Astronomical Imaging:

  • Correcting distortions or degradations in images from telescopes, including those caused by atmospheric turbulence.

  • Enhancing details of celestial bodies.


  • Restoring fingerprints, footprints, or other critical forensic evidence.

  • Enhancing surveillance footage to identify subjects or details.

Film and Video Restoration:

  • Recovering and enhancing old films or videos that have degraded over time.

  • Removing flickers, dust, scratches, or other distortions in video content.

Remote Sensing and Satellite Imaging:

  • Correcting images taken from satellites, drones, or aircraft from atmospheric distortions, sensor noise, etc.

  • Improving clarity and quality of images used for land cover mapping, resource exploration, or environmental monitoring.

Art Restoration:

  • Helping art restorers visualize what damaged paintings or sculptures might have originally looked like.

  • Digital restoration of old or damaged artwork for archival purposes.

Consumer Electronics:

  • Integrated features in smartphones or digital cameras to correct common issues like motion blur.

  • Enhancing images in real-time on television sets to provide better clarity and viewability.

Surveillance and Security:

  • Enhancing nighttime surveillance footage.

  • Restoring details of images from security cameras affected by environmental factors like rain, fog, or smoke.


class ImageRestoration:
    def __init__(self, args):
        Initializes the GFPGANDemo class.

            args: ArgumentParser object containing the necessary arguments.

    def setup_input_output(self):
        Setup input and output directories based on provided arguments.

    def setup_background_upsampler(self):
        Set up the background upsampler based on provided arguments.

            bg_upsampler: The initialized background upsampler.

    def setup_gfpgan_restorer(self):
        Set up the GFPGAN restorer based on provided arguments.

            restorer: The initialized GFPGAN restorer.

    def restore(self, img_path):
        Restore a given image using the GFPGAN model.

            img_path (str): Path to the image that needs to be processed.

    def process_all_images(self):
        Process all images based on the list initialized from input arguments.

    def parse_args():
        Parses command-line arguments.

            args: ArgumentParser object containing the parsed arguments.

if __name__ == '__main__':
    args = GFPGANDemo.parse_args()
    demo = GFPGANDemo(args)
    demo.process_all_images()  # this processes all images in the list

Class Definition - ImageRestoration: The ImageRestoration class is designed to encapsulate the functionality for using the GFPGAN model for image restoration.

Initialization Method: The constructor of the class expects an args parameter, which is an ArgumentParser object containing arguments for configuring the demo. Within the constructor:

  • The provided arguments are stored for use throughout the instance.

  • A method is called to configure input and output directories.

  • The background upsampler and GFPGAN restorer are set up and stored for later use.

Placeholder Methods: There are several methods that are currently placeholders, meant to be fleshed out later. These methods are:

  • A method to configure the input and output directories.

  • A method to set up and return the background upsampler, a component necessary for image restoration.

  • A method to set up and return the GFPGAN restorer, the main component for the image restoration task.

  • A function to restore a given image using the GFPGAN model.

  • A function to process all images based on a list initialized from the provided arguments.

Static Method for Argument Parsing: There's a static method whose purpose is to parse command-line arguments and return them. Being static means it can be called directly on the class without creating an instance.

Main Execution Block: If the script is run as the main program (and not imported elsewhere), the following steps occur:

  • The static method for parsing arguments is called to get the command-line arguments.

  • An instance of the ImageRestoration class is created using the parsed arguments.

  • A method is called on the created instance to process all images specified in the arguments.

We have got more clear image on the right for the corresponding input image on the left.

We have provided only the code template. For a complete implementation, contact us.

If you require assistance with the implementation of the topic mentioned above, or if you need help with related projects, please don't hesitate to reach out to us.
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