# Deep Graph-Convolutional Image Denoising

## TL;DR

This paper introduces a novel neural network architecture using graph convolution layers with dynamically computed graphs to incorporate non-local self-similarity for improved image denoising, achieving state-of-the-art results.

## Contribution

It presents a new end-to-end trainable network with graph convolution layers that dynamically model non-local relationships, enhancing denoising performance over existing methods.

## Key findings

- Achieves state-of-the-art denoising results on synthetic and real noise.
- Demonstrates the effectiveness of dynamic graph convolution in capturing self-similarity.
- Introduces a lightweight Edge-Conditioned Convolution to improve training stability.

## Abstract

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. In this work, the graph is dynamically computed from similarities among the hidden features of the network, so that the powerful representation learning capabilities of the network are exploited to uncover self-similar patterns. We introduce a lightweight Edge-Conditioned Convolution which addresses vanishing gradient and over-parameterization issues of this particular graph convolution. Extensive experiments show state-of-the-art performance with improved qualitative and quantitative results on both synthetic Gaussian noise and real noise.