# CNN: How do I handle Blurred images in the dataset?

I have 30% blurred images in each classes. I have a total of 10 classes. I'm not allowed to drop these blurred images. How do I train the model to get better accuracy for both blurred and nonblurred training dataset ? Currently, I'm at 11% accuracy.

The images were blurred using a Gaussian blur.

I have used a Wiener filter, but not able to restore the image from blurred images.

Please can anyone suggest a good way to train the model.

It would be helpful if you could add a bit more information, e.g. what problem you are solving. Assuming you are solving a classification problem, do these blurred images have any significance, e.g. are they uniformly distributed across classes? One thing that might help is adding some labels, for example in the input in the model. Normal images will have label $$0$$, blurred ones label $$1$$. In general, blurring is a legitimate way of transforming/augmenting the data. Another suggestion is that you can transform other images in the training data (not necessarily with Gaussian blur), to add variation.