dev(narugo): update docs

def _check_environment():

    """

    Check if required dependencies are installed.

    :raises EnvironmentError: If opencv-contrib-python is not available.

    """

    if guidedFilter is None:

        raise EnvironmentError('opencv-contrib-python is not available.\n'

                               'Please install `opencv-contrib-python`.')

    """

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

    This function applies random bilateral filtering and random guided filtering iteratively to reduce adversarial noise

    in the input image.

    This function applies a two-stage filtering process:

    1. Random bilateral filtering for b_iters iterations

    2. Random guided filtering for g_iters iterations

    :param image: The input image.

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

    :param image: The input image to be denoised

    :type image: Image.Image

    :param diameter_min: Minimum diameter for bilateral filtering.

    :param diameter_min: Minimum diameter of pixel neighborhood for bilateral filtering

    :type diameter_min: int, optional

    :param diameter_max: Maximum diameter for bilateral filtering.

    :param diameter_max: Maximum diameter of pixel neighborhood for bilateral filtering

    :type diameter_max: int, optional

    :param sigma_color_min: Minimum filter sigma in the color space for bilateral filtering.

    :param sigma_color_min: Minimum filter sigma in color space for bilateral filtering

    :type sigma_color_min: float, optional

    :param sigma_color_max: Maximum filter sigma in the color space for bilateral filtering.

    :param sigma_color_max: Maximum filter sigma in color space for bilateral filtering

    :type sigma_color_max: float, optional

    :param sigma_space_min: Minimum filter sigma in the coordinate space for bilateral filtering.

    :param sigma_space_min: Minimum filter sigma in coordinate space for bilateral filtering

    :type sigma_space_min: float, optional

    :param sigma_space_max: Maximum filter sigma in the coordinate space for bilateral filtering.

    :param sigma_space_max: Maximum filter sigma in coordinate space for bilateral filtering

    :type sigma_space_max: float, optional

    :param radius_min: Minimum radius for guided filtering.

    :param radius_min: Minimum windows size for guided filtering

    :type radius_min: int, optional

    :param radius_max: Maximum radius for guided filtering.

    :param radius_max: Maximum windows size for guided filtering

    :type radius_max: int, optional

    :param eps_min: Minimum guided filter regularization term.

    :param eps_min: Minimum regularization term for guided filtering

    :type eps_min: float, optional

    :param eps_max: Maximum guided filter regularization term.

    :param eps_max: Maximum regularization term for guided filtering

    :type eps_max: float, optional

    :param b_iters: Number of iterations for bilateral filtering.

    :param b_iters: Number of bilateral filtering iterations

    :type b_iters: int, optional

    :param g_iters: Number of iterations for guided filtering.

    :param g_iters: Number of guided filtering iterations

    :type g_iters: int, optional

    :return: Image with adversarial noise removed.

    :return: Denoised image

    :rtype: 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')

    """

    _check_environment()

    image = load_image(image, mode='RGB', force_background='white')