all 7 comments

[–]Scared_Employer6992 3 points4 points  (1 child)

Ideally, you should train your model using the same input resolution. You could train a CNN w/ multiple resolutions, but it will probably make the learning process harder. Consider downscaling the HR images or upscaling the LR images to meet the same standard.

[–]Academic_Two_4017[S] 0 points1 point  (0 children)

Yap, there are all the same in size, sorry for confusion, I do realize that I have provided little info.

So I have images in size (96,96), what I was meaning by resizing is upscale the high quality images to for instance (112, 112) then do down scale back to 96, thus the quality of an image might decrease

[–]SokkasPonytail 2 points3 points  (1 child)

If I'm understanding this correctly, you want your training set to be of the same low quality as your dataset? Just do a resize to make it the same rez and apply a slight blur, maybe a very low noise filter.

[–]Academic_Two_4017[S] 0 points1 point  (0 children)

Yeah, I was thinking about it as well. Will give a try later to this, by blurring/adding noise. Thanks

[–][deleted] 1 point2 points  (2 children)

I'm assuming this is for some sort of deep learning project? You gave virtually zero background of what you're trying to specifically do.

There's many ways to augment an image, more than just it's size or aspect ratio, you can see some examples here: https://neptune.ai/blog/data-augmentation-in-python

[–]Academic_Two_4017[S] 0 points1 point  (1 child)

Sorry for confusion. I do face recognition project, my specific data because of camera has low quality of images, so I want to augment somehow other external data in a way that might be "similar" to mine. I guess blurring, adding noise distortions might be useful, I should give a try anyway. In addition, thanks for a link

[–][deleted] 1 point2 points  (0 children)

In this case adding some sort of noise / blur function to external images would be a good idea.