Solving Inverse Problems with Hybrid Deep Image Priors: the challenge of preventing overfitting

Understand

We mainly analyze and solve the overfitting problem of deep image prior (DIP).

• Deep image prior can solve inverse problems such as super-resolution, inpainting and denoising.
• The main advantage of DIP over other deep learning approaches is that it does not need access to a large dataset.
• However, due to the large number of parameters of the neural network and noisy data, DIP overfits to the noise in the image as the number of iterations grows.