Non-Local Color Image Denoising with Convolutional Neural Networks

TL;DR

This paper introduces a non-local deep neural network architecture for image denoising that leverages self-similarity in images, achieving state-of-the-art results efficiently for both grayscale and color images.

Contribution

The paper presents a novel non-local deep learning model inspired by variational methods, enhancing denoising performance without increasing network capacity.

Findings

Achieves top denoising performance on Berkeley dataset

Effective for both grayscale and color images across noise levels

Utilizes non-local processing with efficient GPU implementation

Abstract

We propose a novel deep network architecture for grayscale and color image denoising that is based on a non-local image model. Our motivation for the overall design of the proposed network stems from variational methods that exploit the inherent non-local self-similarity property of natural images. We build on this concept and introduce deep networks that perform non-local processing and at the same time they significantly benefit from discriminative learning. Experiments on the Berkeley segmentation dataset, comparing several state-of-the-art methods, show that the proposed non-local models achieve the best reported denoising performance both for grayscale and color images for all the tested noise levels. It is also worth noting that this increase in performance comes at no extra cost on the capacity of the network compared to existing alternative deep network architectures. In addition, we highlight a direct link of the proposed non-local models to convolutional neural networks. This connection is of significant importance since it allows our models to take full advantage of the latest advances on GPU computing in deep learning and makes them amenable to efficient implementations through their inherent parallelism.