torchvision.transforms.functional.gaussian_blur(img: Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None) Tensor[source]

Performs Gaussian blurring on the image by given kernel

The convolution will be using reflection padding corresponding to the kernel size, to maintain the input shape. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means at most one leading dimension.

  • img (PIL Image or Tensor) – Image to be blurred

  • kernel_size (sequence of python:ints or int) –

    Gaussian kernel size. Can be a sequence of integers like (kx, ky) or a single integer for square kernels.


    In torchscript mode kernel_size as single int is not supported, use a sequence of length 1: [ksize, ].

  • sigma (sequence of python:floats or float, optional) –

    Gaussian kernel standard deviation. Can be a sequence of floats like (sigma_x, sigma_y) or a single float to define the same sigma in both X/Y directions. If None, then it is computed using kernel_size as sigma = 0.3 * ((kernel_size - 1) * 0.5 - 1) + 0.8. Default, None.


    In torchscript mode sigma as single float is not supported, use a sequence of length 1: [sigma, ].


Gaussian Blurred version of the image.

Return type:

PIL Image or Tensor

Examples using gaussian_blur:

Illustration of transforms

Illustration of transforms


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