Denoising Images When Training a Classification Model Suppose you have a binary outcome variable and have some training data (10,000 images in jpg format). Also you have a test set of say 11,000 images. If we want to train a classification model and want to improve the image quality (denoise the images), should we do it for every image in the training set? Likewise, should we do it for very image in the test set? Or should the images in training set randomly be chosen to be denoised (likewise in the test set)?
You can denoise a certain fraction of images (preferably 0.25-0.3) randomly for each epoch.
Adding Gaussian noise to images gives better results.Refer this : Link