r/computervision • u/CommandShot1398 • 4d ago

Help: Theory Any quantitative method to determine if an image is noisy?

Hello all

As you can see in the image above, there are noises in the middle image. However, this is a qualitative observation. I have not found any solution to reliably determine if an image is noisy.

The reason I need this is that I am dealing with a segmentation problem, and the model is from a different domain, and there is a lot of variation in the lighting, angles, and video decompression quality. Therefore, there is no reference image, and for a specific part of the pipe, I need to check whether the image is noisy so I can adjust the threshold or apply a smoothing filter to better filter out regions with irrelevant pixel values.

I hope it was clear. I know it is very complicated. Thank you all in advance.

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u/anxiouscsstudent 4d ago

There's some methods https://people.csail.mit.edu/billf/publications/Noise_Estimation_Single_Image.pdf but video compression artifacts are different from sensor noise and are going to be spatially and temporally correlated.

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u/CommandShot1398 4d ago

Hey thanks. Took a quick look and also checked out the papers that have cited this one. From what I found, there is no way to determine that an image is noisy without assuming a few arbitrary priors, which I cannot do.

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u/RelationshipLong9092 4d ago edited 4d ago

if you can make absolutely no assumptions of course you can't do anything with one image

but i bet you can come up with some heuristics that make sense for your application

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u/SteveWeavers 1d ago

In principle it is not complicated. Remove the noise , subtract the result from the original image, measure the result. You need to determine the best way to remove noise and how you want to measure it. For these decisions more information is required. If you have enough samples with the original and the noisy image you could train a denoising auto encoder. If not you will have to choose a more conventional method morphology, convolution , or an FFT filter. If you are measuring the noise to remove it then you are over thinking the problem.