Exploring ensembles and uncertainty minimization in denoising networks

TL;DR

This paper investigates ensemble learning and uncertainty minimization techniques to enhance neural network-based image denoising, introducing a fusion model with attention modules that improves noise removal performance.

Contribution

It proposes a novel fusion model with attention modules for better uncertainty handling and noise reduction in denoising networks, extending existing ensemble methods.

Findings

Improved denoising performance over baseline models

Effective uncertainty minimization through ensemble manipulations

Fusion model with attention modules enhances noise removal

Abstract

The development of neural networks has greatly improved the performance in various computer vision tasks. In the filed of image denoising, convolutional neural network based methods such as DnCNN break through the limits of classical methods, achieving better quantitative results. However, the epistemic uncertainty existing in neural networks limits further improvements in their performance over denoising tasks. Therefore, we develop and study different solutions to minimize uncertainty and further improve the removal of noise. From the perspective of ensemble learning, we implement manipulations to noisy images from the point of view of spatial and frequency domains and then denoise them using pre-trained denoising networks. We propose a fusion model consisting of two attention modules, which focus on assigning the proper weights to pixels and channels. The experimental results show that our model achieves better performance on top of the baseline of regular pre-trained denoising networks.