Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators

Understand

Convolutional Neural Networks (CNNs) have emerged as highly successful tools for image generation, recovery, and restoration.

• A major contributing factor to this success is that convolutional networks impose strong prior assumptions about natural images.
• A surprising experiment that highlights this architectural bias towards natural images is that one can remove noise and corruptions from a natural image without using any training data, by simply fitting (via gradient descent) a randomly initialized, over-parameterized convolutional generator to the corrupted image.
• While this over-parameterized network can fit the corrupted image perfectly, surprisingly after a few iterations of gradient descent it generates an almost uncorrupted image.