CNN: How do I handle Blurred images in the dataset?
I will suggest using data augmentation approaches to even out your data distributions. It will make your blurred images more usable to the model
The data distribution of 30% of your images deviates from the rest because they are blurred. Experiment with training using random blur with appropriate min-max ranges in the data augmentation pipeline (on the images that aren't blurred). This will help the model to smoothly generalize across blurred images. If you don't have labels of which images are blurred, use blur detection algorithms to determine a threshold after which you want to augment.
After doing this, it may be important that you do test-time data augmentation as well.