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MedianBlur

Description

Apply median blur to the input image.

    This transform uses a median filter to blur the input image. Median filtering is particularly
    effective at removing salt-and-pepper noise while preserving edges, making it a popular choice
    for noise reduction in image processing.

    Args:
        blur_limit (int | tuple[int, int]): Maximum aperture linear size for blurring the input image.
            Must be odd and in the range [3, inf).
            - If a single int is provided, the kernel size will be randomly chosen
              between 3 and that value.
            - If a tuple of two ints is provided, it defines the inclusive range
              of possible kernel sizes.
            Default: (3, 7)

        p (float): Probability of applying the transform. Default: 0.5

    Targets:
        image

    Image types:
        uint8, float32

    Number of channels:
        Any

    Note:
        - The kernel size (aperture linear size) must always be odd and greater than 1.
        - Unlike mean blur or Gaussian blur, median blur uses the median of all pixels under
          the kernel area, making it more robust to outliers.
        - This transform is particularly useful for:
          * Removing salt-and-pepper noise
          * Preserving edges while smoothing images
          * Pre-processing images for edge detection algorithms
        - For color images, the median is calculated independently for each channel.
        - Larger kernel sizes result in stronger blurring effects but may also remove
          fine details from the image.

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> transform = A.MedianBlur(blur_limit=(3, 7), p=0.5)
        >>> result = transform(image=image)
        >>> blurred_image = result["image"]

    References:
        - Median filter: https://en.wikipedia.org/wiki/Median_filter
        - OpenCV medianBlur: https://docs.opencv.org/master/d4/d86/group__imgproc__filter.html#ga564869aa33e58769b4469101aac458f9
    

Parameters

  • blur_limit: int | tuple[int, int] (default: 7)
  • p: float (default: 0.5)

Targets

  • Image

Try it out

Original Image:

Original

Result:

Transform result will appear here