Table of Contents
Shortcuts

# gaussian_blur

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

  Performs Gaussian blurring on the image by given kernel. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions.

  Parameters:

    - 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.

      Note

      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.

      Note

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

  Returns:

    Gaussian Blurred version of the image.

  Return type:

    PIL Image or Tensor

## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

## Resources

Find development resources and get your questions answered

View Resources

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.