Deep Graph-Convolutional Image Denoising

Understand

Non-local self-similarity is well-known to be an effective prior for the image denoising problem.

• However, little work has been done to incorporate it in convolutional neural networks, which surpass non-local model-based methods despite only exploiting local information.
• In this paper, we propose a novel end-to-end trainable neural network architecture employing layers based on graph convolution operations, thereby creating neurons with non-local receptive fields.
• The graph convolution operation generalizes the classic convolution to arbitrary graphs.