imgutils.restore.adversarial

Overview:

Useful tools to remove adversarial noises, just using opencv library without any models.

This is an overall benchmark of all the adversarial denoising:

Note

This tool is inspired from Huggingface - mf666/mist-fucker.

remove_adversarial_noise

imgutils.restore.adversarial.remove_adversarial_noise(image: Image, diameter_min: int = 4, diameter_max: int = 6, sigma_color_min: float = 6.0, sigma_color_max: float = 10.0, sigma_space_min: float = 6.0, sigma_space_max: float = 10.0, radius_min: int = 3, radius_max: int = 6, eps_min: float = 16.0, eps_max: float = 24.0, b_iters: int = 64, g_iters: int = 8) → Image

Remove adversarial noise from an image using random bilateral and guided filtering.

This function applies a two-stage filtering process: 1. Random bilateral filtering for b_iters iterations 2. Random guided filtering for g_iters iterations

The randomization of filter parameters helps in better noise removal while preserving image details.

Parameters:

• image (Image.Image) – The input image to be denoised

• diameter_min (int, optional) – Minimum diameter of pixel neighborhood for bilateral filtering

• diameter_max (int, optional) – Maximum diameter of pixel neighborhood for bilateral filtering

• sigma_color_min (float, optional) – Minimum filter sigma in color space for bilateral filtering

• sigma_color_max (float, optional) – Maximum filter sigma in color space for bilateral filtering

• sigma_space_min (float, optional) – Minimum filter sigma in coordinate space for bilateral filtering

• sigma_space_max (float, optional) – Maximum filter sigma in coordinate space for bilateral filtering

• radius_min (int, optional) – Minimum windows size for guided filtering

• radius_max (int, optional) – Maximum windows size for guided filtering

• eps_min (float, optional) – Minimum regularization term for guided filtering

• eps_max (float, optional) – Maximum regularization term for guided filtering

• b_iters (int, optional) – Number of bilateral filtering iterations

• g_iters (int, optional) – Number of guided filtering iterations

Returns:

Denoised image

Return type:

Image.Image

Raises:

EnvironmentError – If opencv-contrib-python is not installed

Example:

>>> from imgutils.restore import remove_adversarial_noise
>>> from PIL import Image
>>>
>>> img = Image.open('noisy_image.png')
>>> cleaned_img = remove_adversarial_noise(img)
>>> cleaned_img.save('cleaned_image.png')