Hi @mjack3 thanks for the request.

We do have GaussianBlur https://github.com/pytorch/vision/blob/main/torchvision/transforms/transforms.py#L1765

May I know what's the difference between gaussian blur and gaussian Noise? In case they are same feel free to use T.GaussianBlur

@oke-aditya maybe the correct link is https://albumentations.ai/docs/api_reference/augmentations/transforms/#albumentations.augmentations.transforms.GaussNoise

Apply gaussian noise to the input image

What do you think @vfdev-5 ? Is this useful to add to torchvision.transforms ?

Gaussian noise and Gaussian blur are different as I am showing below. As I said, Gaussian noise is used in several unsupervised learning methods

I think Salt and Pepper and Gaussian Noise are valid transforms to offer. I would probably be in favour to create them on the new API which is in progress. Wdyt?

Seems great, salt pepper noise and gaussian Noise and probably textBook transforms. Seems nice addition to new API.

A minimal workaround today could be the following (keeping in mind all limitations it could have):

from PIL import Image

import torch
import torchvision


img = Image.open("test-image.jpg")

from torchvision.transforms import Compose, PILToTensor, ToPILImage


# Tensor GN pipeline

def gauss_noise_tensor(img):
    assert isinstance(img, torch.Tensor)
    dtype = img.dtype
    if not img.is_floating_point():
        img = img.to(torch.float32)
    
    sigma = 25.0
    
    out = img + sigma * torch.randn_like(img)
    
    if out.dtype != dtype:
        out = out.to(dtype)
        
    return out


t1 = Compose([
    PILToTensor(),
    gauss_noise_tensor,
    # For visualization purposes
    ToPILImage()
])


o1 = t1(img)

import matplotlib.pyplot as plt
%matplotlib inline


plt.figure(figsize=(14, 7))
plt.subplot(121)
plt.title("Pillow")
plt.imshow(img)
plt.subplot(122)
plt.title("Tensor GN")
plt.imshow(o1)

I agree that we could implement it and its variant in the transforms.

That will the job for now

This seems easy to do with: https://pytorch.org/docs/stable/generated/torch.normal.html ?

It is as easy as developing a horizontal flip. However, having it already implemented would be helpful.