I am working on a project with really noisy images. I have trained a detector that can detect the characters but fails in some cases (noise is high).
So far I have gone through many denoising, deblurring, super-resolution papers. The problem with denoising papers is that in almost all of them, they use a specified Gaussian noise to first add noise and trains the model on that. I have tried it but it doesn't work very well in my domain as the source of the noise in my images is different.
Let's say I have few thousand images (real-data with noise), is there any deep learning/image processing approach which helps me to get a noise map which I'll use to augment my clean images so that I can train denoising models.